eBay:iPads:: startprice regression:: csmmdl_sp
bdanalytics
Date: (Mon) Jul 27, 2015
Introduction:
Data: Source: Training: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv
New: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Regression results: First run:
Classification results: template: prdline.my == “Unknown” -> 296 Low.cor.X.glm: Leaderboard: 0.83458 newobs_tbl=[N=471, Y=327]; submit_filename=template_Final_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=76]=201; max.Accuracy.OOB=0.7710; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=95.42; productline=49.22; D.T.like=29.75; D.T.use=26.32; D.T.box=21.53;
prdline: -> Worse than template prdline.my == “Unknown” -> 285 All.X.no.rnorm.rf: Leaderboard: 0.82649 newobs_tbl=[N=485, Y=313]; submit_filename=prdline_Final_rf_submit.csv OOB_conf_mtrx=[YN=119, NY=80]=199; max.Accuracy.OOB=0.8339; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=84.25; D.sum.TfIdf=7.28; D.T.use=4.26; D.T.veri=2.78; D.T.scratch=1.99; D.T.box=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.81234 newobs_tbl=[N=471, Y=327]; submit_filename=prdline_Low_cor_X_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=74]=199; max.Accuracy.OOB=0.8205; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=96.07; prdline.my=51.37; D.T.like=29.39; D.T.use=25.43; D.T.box=22.27; D.T.veri=; D.T.scratch=;
oobssmpl: -> Low.cor.X.glm: Leaderboard: 0.83402 newobs_tbl=[N=440, Y=358]; submit_filename=oobsmpl_Final_glm_submit OOB_conf_mtrx=[YN=114, NY=84]=198; max.Accuracy.OOB=0.7780; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=93.87; prdline.my=60.48; D.sum.TfIdf=; D.T.condition=8.69; D.T.screen=7.96; D.T.use=7.50; D.T.veri=; D.T.scratch=;
category: -> Low.cor.X.glm: Leaderboard: 0.82381 newobs_tbl=[N=470, Y=328]; submit_filename=category_Final_glm_submit OOB_conf_mtrx=[YN=119, NY=57]=176; max.Accuracy.OOB=0.8011; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=79.19; prdline.my=55.22; D.sum.TfIdf=; D.T.ipad=27.05; D.T.like=21.44; D.T.box=20.67; D.T.condition=; D.T.screen=;
dataclns: -> All.X.no.rnorm.rf: Leaderboard: 0.82211 newobs_tbl=[N=485, Y=313]; submit_filename=dataclns_Final_rf_submit OOB_conf_mtrx=[YN=104, NY=75]=179; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=65.85; prdline.my=7.74; D.sum.TfIdf=; D.T.use=2.01; D.T.condition=1.87; D.T.veri=1.62; D.T.ipad=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.79264 newobs_tbl=[N=460, Y=338]; submit_filename=dataclns_Low_cor_X_glm_submit OOB_conf_mtrx=[YN=113, NY=74]=187; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 -> different from prev run of 0.6 biddable=100.00; startprice.log=91.85; prdline.my=38.34; D.sum.TfIdf=; D.T.ipad=29.92; D.T.box=27.76; D.T.work=25.79; D.T.use=; D.T.condition=;
txtterms: -> top_n = c(10) Low.cor.X.glm: Leaderboard: 0.81448 newobs_tbl=[N=442, Y=356]; submit_filename=txtterms_Final_glm_submit OOB_conf_mtrx=[YN=113, NY=69]=182; max.Accuracy.OOB=0.7943; opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=90.11; prdline.my=37.65; D.sum.TfIdf=; D.T.ipad=28.67; D.T.work=24.90; D.T.great=21.44; # [1] “D.T.condit” “D.T.condition” “D.T.good” “D.T.ipad” “D.T.new”
# [6] “D.T.scratch” “D.T.screen” “D.T.this” “D.T.use” “D.T.work”
All.X.glm: Leaderboard: 0.81016
newobs_tbl=[N=445, Y=353]; submit_filename=txtterms_Final_glm_submit
OOB_conf_mtrx=[YN=108, NY=72]=180; max.Accuracy.OOB=0.7966;
opt.prob.threshold.OOB=0.5
biddable=100.00; startprice.log=88.24; prdline.my=33.81; D.sum.TfIdf=;
D.T.scratch=25.51; D.T.use=18.97; D.T.good=16.37; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.great” “D.T.excel” “D.T.work” “D.T.ipad”
Max.cor.Y.rpart: Leaderboard: 0.79258
newobs_tbl=[N=439, Y=359]; submit_filename=txtterms_Final_rpart_submit
OOB_conf_mtrx=[YN=105, NY=76]=181; max.Accuracy.OOB=0.7954802;
opt.prob.threshold.OOB=0.5
startprice.log=100; biddable=; prdline.my=; D.sum.TfIdf=;
D.T.scratch=; D.T.use=; D.T.good=; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.no.rnorm.rf: Leaderboard: 0.80929
newobs_tbl=[N=545, Y=253]; submit_filename=txtterms_Final_rf_submit
OOB_conf_mtrx=[YN=108, NY=61]=169; max.Accuracy.OOB=0.8090395
opt.prob.threshold.OOB=0.5
startprice.log=100.00; biddable=78.82; idseq.my=63.43; prdline.my=45.57;
D.T.use=2.76; D.T.condit=2.35; D.T.scratch=2.00; D.T.good=; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
txtclstr: All.X.no.rnorm.rf: Leaderboard: 0.79363 -> 0.79573 newobs_tbl=[N=537, Y=261]; submit_filename=txtclstr_Final_rf_submit OOB_conf_mtrx=[YN=104, NY=61]=165; max.Accuracy.OOB=0.8135593 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=79.99; idseq.my=64.94; prdline.my=4.14; prdline.my.clusterid=1.15; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
dupobs: All.X.no.rnorm.rf: Leaderboard: 0.79295 newobs_tbl=[N=541, Y=257]; submit_filename=dupobs_Final_rf_submit OOB_conf_mtrx=[YN=114, NY=65]=179; max.Accuracy.OOB=0.7977401 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=94.49; idseq.my=67.40; prdline.my=4.48; prdline.my.clusterid=1.99; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.no.rnorm.rf: Leaderboard: 0.79652
newobs_tbl=[N=523, Y=275]; submit_filename=dupobs_Final_rf_submit
OOB_conf_mtrx=[YN=114, NY=65]=179; max.Accuracy.OOB=0.7977401
opt.prob.threshold.OOB=0.5
startprice.log=100.00; biddable=94.24; idseq.my=67.92;
prdline.my=4.33; prdline.my.clusterid=2.17; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
csmmdl: All.X.no.rnorm.rf: Leaderboard: 0.79396 newobs_tbl=[N=525, Y=273]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=111, NY=66]=177; max.Accuracy.OOB=0.8000000 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=90.30; idseq.my=67.06; prdline.my=4.40; cellular.fctr=3.57; prdline.my.clusterid=2.08;
All.Interact.X.no.rnorm.rf: Leaderboard: 0.77867 newobs_tbl=[N=564, Y=234]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=120, NY=53]=173; max.Accuracy.OOB=0.8045198 opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=93.99; idseq.my=57.30; prdline.my=9.09; cellular.fctr=3.30; prdline.my.clusterid=2.35; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
Prediction Accuracy Enhancement Options:
- import.data chunk:
- which obs should be in fit vs. OOB (currently dirty.0 vs .1 is split 50%)
- inspect.data chunk:
- For date variables
- Appropriate factors ?
- Different / More last* features ?
- For date variables
- scrub.data chunk:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
- extract.features chunk:
- Text variables: move to date extraction chunk ???
- Mine acronyms
- Mine places
- Text variables: move to date extraction chunk ???
- Review set_global_options chunk after features are finalized
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glb_txt_vars
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Convert user-specified mutate code to config specs
- Add n-grams for glb_txt_vars
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- move model_type parameter to myfit_mdl before indep_vars_vctr (keep all model_* together)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- Skip trControl.method=“cv” for dummy classifier ?
- Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Replicate myfit_mdl_classification features in myfit_mdl_regression
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list=ls())
set.seed(12345)
options(stringsAsFactors=FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
registerDoMC(4) # max(length(glb_txt_vars), glb_n_cv_folds) + 1
#packageVersion("tm")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
glb_trnng_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv"
glb_newdt_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv"
glb_out_pfx <- "csmmdl_sp_"
glb_save_envir <- FALSE # or TRUE
glb_is_separate_newobs_dataset <- TRUE # or TRUE
glb_split_entity_newobs_datasets <- TRUE # or FALSE
glb_split_newdata_method <- "sample" # "condition" or "sample" or "copy"
glb_split_newdata_condition <- NULL # or "is.na(<var>)"; "<var> <condition_operator> <value>"
glb_split_newdata_size_ratio <- 0.3 # > 0 & < 1
glb_split_sample.seed <- 123 # or any integer
glb_max_fitobs <- NULL # or any integer
glb_is_regression <- TRUE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE #or FALSE
glb_rsp_var_raw <- "startprice"
# for classification, the response variable has to be a factor
glb_rsp_var <- glb_rsp_var_raw #"sold.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- NULL #function(raw) {
# return(log(raw))
# ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == 1, "Y", "N"); return(relevel(as.factor(ret_vals), ref="N"))
# #as.factor(paste0("B", raw))
# #as.factor(gsub(" ", "\\.", raw))
# }
# glb_map_rsp_raw_to_var(c(1, 1, 0, 0, NA))
glb_map_rsp_var_to_raw <- NULL #function(var) {
# return(exp(var))
# as.numeric(var) - 1
# #as.numeric(var)
# #gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# #c(FALSE, TRUE)[as.numeric(var)]
# }
# glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(c(1, 1, 0, 0, NA)))
if ((glb_rsp_var != glb_rsp_var_raw) & is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # model_id is appended later
# List info gathered for various columns
# <col_name>: <description>; <notes>
# description = The text description of the product provided by the seller.
# biddable = Whether this is an auction (biddable=1) or a sale with a fixed price (biddable=0).
# startprice = The start price (in US Dollars) for the auction (if biddable=1) or the sale price (if biddable=0).
# condition = The condition of the product (new, used, etc.)
# cellular = Whether the iPad has cellular connectivity (cellular=1) or not (cellular=0).
# carrier = The cellular carrier for which the iPad is equipped (if cellular=1); listed as "None" if cellular=0.
# color = The color of the iPad.
# storage = The iPad's storage capacity (in gigabytes).
# productline = The name of the product being sold.
# If multiple vars are parts of id, consider concatenating them to create one id var
# If glb_id_var == NULL, ".rownames <- row.names()" is the default
# Derive a numeric feature from id var
glb_id_var <- c("UniqueID")
glb_category_var <- c("prdline.my")
glb_drop_vars <- c(NULL) # or c("<col_name>")
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
glb_assign_pairs_lst <- NULL;
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
# to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)
# Derived features
glb_derive_lst <- NULL;
# Add logs of numerics that are not distributed normally -> do automatically ???
glb_derive_lst[["idseq.my"]] <- list(
mapfn=function(UniqueID) { return(UniqueID - 10000) }
, args=c("UniqueID"))
glb_derive_lst[["prdline.my"]] <- list(
mapfn=function(productline) { return(productline) }
, args=c("productline"))
glb_derive_lst[["startprice.log"]] <- list(
mapfn=function(startprice) { return(log(startprice)) }
, args=c("startprice"))
# glb_derive_lst[["startprice.log.zval"]] <- list(
glb_derive_lst[["descr.my"]] <- list(
mapfn=function(description) { mod_raw <- description;
# Modifications for this exercise only
# Add dictionary to stemDocument e.g. stickers stemmed to sticker ???
mod_raw <- gsub("\\.\\.", "\\. ", mod_raw);
mod_raw <- gsub("(\\w)(\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
mod_raw <- gsub("8\\.25", "825", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" 10\\.SCREEN ", " 10\\. SCREEN ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" actuuly ", " actual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Apple care ", " Applecare ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" ans ", " and ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" bacK!wiped ", " bacK ! wiped ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" backplate", " back plate", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" bend ", " bent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("Best Buy", "BestBuy", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" black\\.Device ", " black \\. Device ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" blocks", " blocked", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" carefully ", " careful ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" conditon|condtion|conditions", " condition", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("(CONDITION|ONLY)\\.(\\w)", "\\1\\. \\2", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("(condition)(Has)", "\\1\\. \\2", mod_raw);
mod_raw <- gsub(" consist ", " consistent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" cracksNo ", " cracks No ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" DEFAULTING ", " DEFAULT ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" definitely ", " definite ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" described", " describe", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" desciption", " description", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" devices", " device", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Digi\\.", " Digitizer\\.", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" display\\.New ", " display\\. New ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" displays", " display", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" drop ", " dropped ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" effect ", " affect ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Excellant ", " Excellent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" excellently", " excellent", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" EUC ", " excellent used condition", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" feels ", " feel ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" fineiCloud ", " fine iCloud ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^Gentle ", "Gently ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" GREAT\\.SCreen ", " GREAT\\. SCreen ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" Framing ", " Frame ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("iCL0UD", "iCL0UD", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^iPad Black 3rd generation ", "iPad 3 Black ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" install\\. ", " installed\\. ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("inivisible", "invisible", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" manuals ", " manual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" book ", " manual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" mars ", " marks ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" minimum", " minimal", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" MINT\\.wiped ", " MINT\\. wiped ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" NEW\\!(SCREEN|ONE) ", " NEW\\! \\1 ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" new looking$", " looks new", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" newer ", " new ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" opening", " opened", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" operated", " operational", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" perfectlycord ", " perfectly cord ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" performance", " performs", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" personalized ", " personal ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" products ", " product ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Keeped ", " Kept ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" knicks ", " nicks ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^READiPad ", "READ iPad ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" REFURB\\.", " REFURBISHED\\.", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" reponding", " respond", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" rotation ", " rotate ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Sales ", " Sale ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" scratchs ", " scratches ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" SCREEB ", " SCREEN ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" shipped| Shipment", " ship", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("shrink wrap", "shrinkwrap", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" sides ", " side ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" skinned,", " skin,", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" spec ", " speck ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^somescratches ", "some scratches ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Sticker ", " Stickers ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("SWAPPA\\.COM", "SWAPPACOM", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" T- Mobile", " TMobile", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" touchscreen ", " touch screen ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" use\\.Scratches ", " use\\. Scratches ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" verify ", " verified ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" wear\\.Device ", " wear\\. Device ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" whats ", " what's ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" WiFi\\+4G ", " WiFi \\+ 4G ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Zaag Invisible Shield", " Zaag InvisibleShield", mod_raw,
ignore.case=TRUE);
return(mod_raw) }
, args=c("description"))
# mapfn=function(startprice) { return(scale(log(startprice))) }
# , args=c("startprice"))
# mapfn=function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn=function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn=function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn=function(Week) { return(substr(Week, 1, 10)) }
# mapfn=function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# , args=c("raw"))
# mapfn=function(PTS, oppPTS) { return(PTS - oppPTS) }
# , args=c("PTS", "oppPTS"))
# # If glb_allobs_df is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glb_allobs_df)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glb_derive_lst[["<txt_var>.niso8859.log"]] <- list(
# mapfn=function(<txt_var>) { match_lst <- gregexpr("&#[[:digit:]]{3};", <txt_var>)
# match_num_vctr <- unlist(lapply(match_lst,
# function(elem) length(elem)))
# return(log(1 + match_num_vctr)) }
# , args=c("<txt_var>"))
# mapfn=function(raw) { mod_raw <- raw;
# mod_raw <- gsub("&#[[:digit:]]{3};", " ", mod_raw);
# # Modifications for this exercise only
# mod_raw <- gsub("\\bgoodIn ", "good In", mod_raw);
# return(mod_raw)
# # Create user-specified pattern vectors
# #sum(mycount_pattern_occ("Metropolitan Diary:", glb_allobs_df$Abstract) > 0)
# if (txt_var %in% c("Snippet", "Abstract")) {
# txt_X_df[, paste0(txt_var_pfx, ".P.metropolitan.diary.colon")] <-
# as.integer(0 + mycount_pattern_occ("Metropolitan Diary:",
# glb_allobs_df[, txt_var]))
#summary(glb_allobs_df[ ,grep("P.on.this.day", names(glb_allobs_df), value=TRUE)])
# glb_derive_lst[["<var1>"]] <- glb_derive_lst[["<var2>"]]
glb_derive_vars <- names(glb_derive_lst)
# tst <- "descr.my"; args_lst <- NULL; for (arg in glb_derive_lst[[tst]]$args) args_lst[[arg]] <- glb_allobs_df[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glb_derive_lst[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glb_date_vars <- NULL # or c("<date_var>")
glb_date_fmts <- list(); #glb_date_fmts[["<date_var>"]] <- "%m/%e/%y"
glb_date_tzs <- list(); #glb_date_tzs[["<date_var>"]] <- "America/New_York"
#grep("America/New", OlsonNames(), value=TRUE)
glb_txt_vars <- c("descr.my")
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"glb_txt_munge_filenames_pfx <- "ebay_mytxt_"
glb_append_stop_words <- list()
# Remember to use unstemmed words
#orderBy(~ -cor.y.abs, subset(glb_feats_df, grepl("[HSA]\\.T\\.", id) & !is.na(cor.high.X)))
glb_append_stop_words[["descr.my"]] <- c(NULL
# freq = 1
,"511","825","975"
,"2nd"
,"a1314","a1430","a1432"
,"abused","across","adaptor","add","antenna","anti","anyone", "area","arizona","att"
,"beginning","bidder","bonus","boot","bound","bruises"
,"changed","changing","chrome"
,"confidence","considerable","consumer","contents","control","cream"
,"date","daughter","decent","defender","defense","degree","depicted"
,"disclaimer","distressed","divider"
,"dlxnqat9g5wt","done","dont","durable","dust","duty"
,"either","erased","ereader","essentially","every","exact"
,"faint","film","final","flickers","folding"
,"generic","genuine","glitter","goes"
,"half","handstand","hdmi","high","higher","hole","hospital"
,"impact","instead","interior"
,"jack","july"
,"keeps","kind","known"
,"last","late","let","letters","level","lifting","limited","line","lining","liquid"
,"local","long","longer","looping","loss"
,"mb292ll","mc707ll","mc916ll","mc991ll","md789ll","mf432ll","mgye2ll"
,"middle", "mind","mixed"
,"neither","none","november"
,"occasional","online","outside"
,"paperwork","period","pet","played","plug","poor","portion","pouch","price","provided"
,"ranging"
,"recently","red","reflected","repeat","required","reserve","residue","result"
,"roughly","running"
,"said","seconds","seem","semi","send","serious","setup"
,"shell","short","size","slice","smoke","smooth"
,"softer","software","somewhat","soon"
,"sparingly","sparkiling","special","speed"
,"stains","standup","status","stopped","strictly","subtle","sustained","swappacom"
,"technical","tempered","texture","thank","therefore","think","though"
,"toddler","totally","touchy","tried","typical"
,"university","unknown","untouched","upgrade"
,"valid","vary","version"
,"want","website","winning","wrapped"
,"zaag","zero", "zombie"
)
#subset(glb_allobs_df, S.T.newyorktim > 0)[, c("UniqueID", "Snippet", "S.T.newyorktim")]
#glb_txt_lst[["Snippet"]][which(glb_allobs_df$UniqueID %in% c(8394, 8317, 8339, 8350, 8307))]
glb_important_terms <- list()
# Remember to use stemmed terms
glb_filter_txt_terms <- "top" # or "sparse"
glb_top_n <- c(10)
names(glb_top_n) <- glb_txt_vars
glb_sprs_thresholds <- c(0.950) # Generates 10 terms
# Properties:
# numrows(glb_feats_df) << numrows(glb_fitobs_df)
# Select terms that appear in at least 0.2 * O(FP/FN(glb_OOBobs_df))
# numrows(glb_OOBobs_df) = 1.1 * numrows(glb_newobs_df)
names(glb_sprs_thresholds) <- glb_txt_vars
# User-specified exclusions
glb_exclude_vars_as_features <- c("productline", "description", "startprice"
, "startprice.log", "sold"
)
if (glb_rsp_var_raw != glb_rsp_var)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_rsp_var_raw)
# List feats that shd be excluded due to known causation by prediction variable
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(NULL)) # or c("<col_name>")
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- TRUE
glb_interaction_only_features <- NULL # or ???
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression)
glb_models_method_vctr <- c("lm", "glm", "bayesglm", "glmnet", "rpart", "rf") else
# Classification
if (glb_is_binomial)
glb_models_method_vctr <- c("glm", "bayesglm", "glmnet", "rpart", "rf") else
glb_models_method_vctr <- c("rpart", "rf")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")
glb_model_metric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glb_model_metric <- NULL # or "<metric_name>"
glb_model_metric_maximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glb_model_metric_smmry <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glb_model_metric_terms)
# metric <- sum(confusion_mtrx * glb_model_metric_terms) / nrow(data)
# names(metric) <- glb_model_metric
# return(metric)
# }
glb_tune_models_df <-
rbind(
#data.frame(parameter="cp", min=0.00005, max=0.00005, by=0.000005),
#seq(from=0.01, to=0.01, by=0.01)
#data.frame(parameter="mtry", min=080, max=100, by=10),
#data.frame(parameter="mtry", min=08, max=10, by=1),
data.frame(parameter="dummy", min=2, max=4, by=1)
)
# or NULL
glb_n_cv_folds <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glb_model_evl_criteria <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glb_model_evl_criteria <-
c("max.Accuracy.OOB", "max.auc.OOB", "max.Kappa.OOB", "min.aic.fit") else
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
glb_sel_mdl_id <- NULL #"Low.cor.X.glm"
glb_fin_mdl_id <- glb_sel_mdl_id # or "Final"
# Depict process
glb_analytics_pn <- petrinet(name="glb_analytics_pn",
trans_df=data.frame(id=1:6,
name=c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 8.739 NA NAStep 1.0: import data
chunk option: eval=
#glb_chunks_df <- myadd_chunk(NULL, "import.data")
glb_trnobs_df <- myimport_data(url=glb_trnng_url, comment="glb_trnobs_df",
force_header=TRUE)## [1] "Reading file ./data/eBayiPadTrain.csv..."
## [1] "dimensions of data in ./data/eBayiPadTrain.csv: 1,861 rows x 11 cols"
## description
## 1 iPad is in 8.5+ out of 10 cosmetic condition!
## 2 Previously used, please read description. May show signs of use such as scratches to the screen and
## 3
## 4
## 5 Please feel free to buy. All products have been thoroughly inspected, cleaned and tested to be 100%
## 6
## biddable startprice condition cellular carrier color
## 1 0 159.99 Used 0 None Black
## 2 1 0.99 Used 1 Verizon Unknown
## 3 0 199.99 Used 0 None White
## 4 0 235.00 New other (see details) 0 None Unknown
## 5 0 199.99 Seller refurbished Unknown Unknown Unknown
## 6 1 175.00 Used 1 AT&T Space Gray
## storage productline sold UniqueID
## 1 16 iPad 2 0 10001
## 2 16 iPad 2 1 10002
## 3 16 iPad 4 1 10003
## 4 16 iPad mini 2 0 10004
## 5 Unknown Unknown 0 10005
## 6 32 iPad mini 2 1 10006
## description
## 65
## 283 Pristine condition, comes with a case and stylus.
## 948 \211\333\317Used Apple Ipad 16 gig 1st generation in Great working condition and 100% functional.Very little
## 1354
## 1366 Item still in complete working order, minor scratches, normal wear and tear but no damage. screen is
## 1840
## biddable startprice condition cellular carrier color
## 65 0 195.00 Used 0 None Unknown
## 283 1 20.00 Used 0 None Unknown
## 948 0 110.00 Seller refurbished 0 None Black
## 1354 0 300.00 Used 0 None White
## 1366 1 125.00 Used Unknown Unknown Unknown
## 1840 0 249.99 Used 1 Sprint Space Gray
## storage productline sold UniqueID
## 65 16 iPad mini 0 10065
## 283 64 iPad 1 0 10283
## 948 32 iPad 1 0 10948
## 1354 16 iPad Air 1 11354
## 1366 Unknown iPad 1 1 11366
## 1840 16 iPad Air 1 11840
## description
## 1856 Overall item is in good condition and is fully operational and ready to use. Comes with box and
## 1857 Used. Tested. Guaranteed to work. Physical condition grade B+ does have some light scratches and
## 1858 This item is brand new and was never used; however, the box and/or packaging has been opened.
## 1859
## 1860 This unit has minor scratches on case and several small scratches on the display. \nIt is in
## 1861 30 Day Warranty. Fully functional engraved iPad 1st Generation with signs of normal wear which
## biddable startprice condition cellular carrier
## 1856 0 89.50 Used 1 AT&T
## 1857 0 239.95 Used 0 None
## 1858 0 329.99 New other (see details) 0 None
## 1859 0 400.00 New 0 None
## 1860 0 89.00 Seller refurbished 0 None
## 1861 0 119.99 Used 1 AT&T
## color storage productline sold UniqueID
## 1856 Unknown 16 iPad 1 0 11856
## 1857 Black 32 iPad 4 1 11857
## 1858 Space Gray 16 iPad Air 0 11858
## 1859 Gold 16 iPad mini 3 0 11859
## 1860 Black 64 iPad 1 1 11860
## 1861 Black 64 iPad 1 0 11861
## 'data.frame': 1861 obs. of 11 variables:
## $ description: chr "iPad is in 8.5+ out of 10 cosmetic condition!" "Previously used, please read description. May show signs of use such as scratches to the screen and " "" "" ...
## $ biddable : int 0 1 0 0 0 1 1 0 1 1 ...
## $ startprice : num 159.99 0.99 199.99 235 199.99 ...
## $ condition : chr "Used" "Used" "Used" "New other (see details)" ...
## $ cellular : chr "0" "1" "0" "0" ...
## $ carrier : chr "None" "Verizon" "None" "None" ...
## $ color : chr "Black" "Unknown" "White" "Unknown" ...
## $ storage : chr "16" "16" "16" "16" ...
## $ productline: chr "iPad 2" "iPad 2" "iPad 4" "iPad mini 2" ...
## $ sold : int 0 1 1 0 0 1 1 0 1 1 ...
## $ UniqueID : int 10001 10002 10003 10004 10005 10006 10007 10008 10009 10010 ...
## - attr(*, "comment")= chr "glb_trnobs_df"
## NULL# glb_trnobs_df <- read.delim("data/hygiene.txt", header=TRUE, fill=TRUE, sep="\t",
# fileEncoding='iso-8859-1')
# glb_trnobs_df <- read.table("data/hygiene.dat.labels", col.names=c("dirty"),
# na.strings="[none]")
# glb_trnobs_df$review <- readLines("data/hygiene.dat", n =-1)
# comment(glb_trnobs_df) <- "glb_trnobs_df"
# glb_trnobs_df <- data.frame()
# for (symbol in c("Boeing", "CocaCola", "GE", "IBM", "ProcterGamble")) {
# sym_trnobs_df <-
# myimport_data(url=gsub("IBM", symbol, glb_trnng_url), comment="glb_trnobs_df",
# force_header=TRUE)
# sym_trnobs_df$Symbol <- symbol
# glb_trnobs_df <- myrbind_df(glb_trnobs_df, sym_trnobs_df)
# }
# glb_trnobs_df <-
# glb_trnobs_df %>% dplyr::filter(Year >= 1999)
if (glb_is_separate_newobs_dataset) {
glb_newobs_df <- myimport_data(url=glb_newdt_url, comment="glb_newobs_df",
force_header=TRUE)
# To make plots / stats / checks easier in chunk:inspectORexplore.data
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df);
comment(glb_allobs_df) <- "glb_allobs_df"
} else {
glb_allobs_df <- glb_trnobs_df; comment(glb_allobs_df) <- "glb_allobs_df"
if (!glb_split_entity_newobs_datasets) {
stop("Not implemented yet")
glb_newobs_df <- glb_trnobs_df[sample(1:nrow(glb_trnobs_df),
max(2, nrow(glb_trnobs_df) / 1000)),]
} else if (glb_split_newdata_method == "condition") {
glb_newobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=glb_split_newdata_condition)))
glb_trnobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=paste0("!(",
glb_split_newdata_condition,
")"))))
} else if (glb_split_newdata_method == "sample") {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=(1-glb_split_newdata_size_ratio))
glb_newobs_df <- glb_trnobs_df[!split, ]
glb_trnobs_df <- glb_trnobs_df[split ,]
} else if (glb_split_newdata_method == "copy") {
glb_trnobs_df <- glb_allobs_df
comment(glb_trnobs_df) <- "glb_trnobs_df"
glb_newobs_df <- glb_allobs_df
comment(glb_newobs_df) <- "glb_newobs_df"
} else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")
comment(glb_newobs_df) <- "glb_newobs_df"
myprint_df(glb_newobs_df)
str(glb_newobs_df)
if (glb_split_entity_newobs_datasets) {
myprint_df(glb_trnobs_df)
str(glb_trnobs_df)
}
} ## [1] "Reading file ./data/eBayiPadTest.csv..."
## [1] "dimensions of data in ./data/eBayiPadTest.csv: 798 rows x 10 cols"
## description
## 1 like new
## 2 Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though
## 3 This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer.
## 4
## 5 Grade A condition means that the Ipad is 100% working condition. Cosmetically 8/9 out of 10 - Will
## 6 Brand new factory sealed iPad in an OPEN BOX...THE BOX ITSELF IS HEAVILY DISTRESSED(see
## biddable startprice condition cellular carrier color
## 1 0 105.00 Used 1 AT&T Unknown
## 2 0 195.00 Used 0 None Unknown
## 3 0 219.99 Used 0 None Unknown
## 4 1 100.00 Used 0 None Unknown
## 5 0 210.99 Manufacturer refurbished 0 None Black
## 6 0 514.95 New other (see details) 0 None Gold
## storage productline UniqueID
## 1 32 iPad 1 11862
## 2 16 iPad mini 2 11863
## 3 64 iPad 3 11864
## 4 16 iPad mini 11865
## 5 32 iPad 3 11866
## 6 64 iPad Air 2 11867
## description
## 1 like new
## 142 iPad mini 1st gen wi-fi 16gb is in perfect working order.
## 309 In excellent condition. Minor scratches on the back. Screen in mint condition. Comes in original
## 312 iPad is in Great condition, the screen is in great condition showing only a few minor scratches, the
## 320 Good condition and fully functional
## 369
## biddable startprice condition cellular carrier color storage
## 1 0 105.00 Used 1 AT&T Unknown 32
## 142 1 0.99 Used 0 None Unknown 16
## 309 0 200.00 Used 1 AT&T Black 32
## 312 1 0.99 Used 0 None Unknown 16
## 320 1 60.00 Used 0 None White 16
## 369 1 197.97 Used 0 None Unknown 64
## productline UniqueID
## 1 iPad 1 11862
## 142 iPad mini 12003
## 309 iPad 3 12170
## 312 iPad mini 2 12173
## 320 iPad 1 12181
## 369 iPad mini 3 12230
## description
## 793 Crack on digitizer near top. Top line of digitizer does not respond to touch. Other than that, all
## 794
## 795
## 796
## 797
## 798 Slightly Used. Includes everything you need plus a nice leather case!\nThere is a slice mark on the
## biddable startprice condition cellular carrier color
## 793 0 104.00 For parts or not working 1 Unknown Black
## 794 0 95.00 Used 1 AT&T Unknown
## 795 1 199.99 Manufacturer refurbished 0 None White
## 796 0 149.99 Used 0 None Unknown
## 797 0 7.99 New Unknown Unknown Unknown
## 798 0 139.00 Used 1 Unknown Black
## storage productline UniqueID
## 793 16 iPad 2 12654
## 794 64 iPad 1 12655
## 795 16 iPad 4 12656
## 796 16 iPad 2 12657
## 797 Unknown iPad 3 12658
## 798 32 Unknown 12659
## 'data.frame': 798 obs. of 10 variables:
## $ description: chr "like new" "Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though " "This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer." "" ...
## $ biddable : int 0 0 0 1 0 0 0 0 0 1 ...
## $ startprice : num 105 195 220 100 211 ...
## $ condition : chr "Used" "Used" "Used" "Used" ...
## $ cellular : chr "1" "0" "0" "0" ...
## $ carrier : chr "AT&T" "None" "None" "None" ...
## $ color : chr "Unknown" "Unknown" "Unknown" "Unknown" ...
## $ storage : chr "32" "16" "64" "16" ...
## $ productline: chr "iPad 1" "iPad mini 2" "iPad 3" "iPad mini" ...
## $ UniqueID : int 11862 11863 11864 11865 11866 11867 11868 11869 11870 11871 ...
## - attr(*, "comment")= chr "glb_newobs_df"
## NULLif ((num_nas <- sum(is.na(glb_trnobs_df[, glb_rsp_var_raw]))) > 0)
stop("glb_trnobs_df$", glb_rsp_var_raw, " contains NAs for ", num_nas, " obs")
if (nrow(glb_trnobs_df) == nrow(glb_allobs_df))
warning("glb_trnobs_df same as glb_allobs_df")
if (nrow(glb_newobs_df) == nrow(glb_allobs_df))
warning("glb_newobs_df same as glb_allobs_df")
if (length(glb_drop_vars) > 0) {
warning("dropping vars: ", paste0(glb_drop_vars, collapse=", "))
glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df), glb_drop_vars)]
glb_trnobs_df <- glb_trnobs_df[, setdiff(names(glb_trnobs_df), glb_drop_vars)]
glb_newobs_df <- glb_newobs_df[, setdiff(names(glb_newobs_df), glb_drop_vars)]
}
#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Combine trnent & newobs into glb_allobs_df for easier manipulation
glb_trnobs_df$.src <- "Train"; glb_newobs_df$.src <- "Test";
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, ".src")
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df)
comment(glb_allobs_df) <- "glb_allobs_df"
# Check for duplicates in glb_id_var
if (length(glb_id_var) == 0) {
warning("using .rownames as identifiers for observations")
glb_allobs_df$.rownames <- rownames(glb_allobs_df)
glb_trnobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Train"))
glb_newobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Test"))
glb_id_var <- ".rownames"
}
if (sum(duplicated(glb_allobs_df[, glb_id_var, FALSE])) > 0)
stop(glb_id_var, " duplicated in glb_allobs_df")
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_id_var)
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_trnobs_df <- glb_newobs_df <- NULL
# For Tableau
write.csv(glb_allobs_df, "data/eBayiPadAll.csv", row.names=FALSE)
#stop(here")
glb_drop_obs <- c(
11234, #sold=0; 2 other dups(10306, 11503) are sold=1
11844, #sold=0; 3 other dups(11721, 11738, 11812) are sold=1
NULL)
glb_allobs_df <- glb_allobs_df[!glb_allobs_df[, glb_id_var] %in% glb_drop_obs, ]
# Make any data corrections here
glb_allobs_df[glb_allobs_df[, glb_id_var] == 10986, "cellular"] <- "1"
glb_allobs_df[glb_allobs_df[, glb_id_var] == 10986, "carrier"] <- "T-Mobile"
# Check for duplicates by all features
require(gdata)## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
##
## The following object is masked from 'package:stats':
##
## nobs
##
## The following object is masked from 'package:utils':
##
## object.size#print(names(glb_allobs_df))
dup_allobs_df <- glb_allobs_df[duplicated2(subset(glb_allobs_df,
select=-c(UniqueID, sold, .src))), ]
dup_allobs_df <- orderBy(~productline+description+startprice+biddable, dup_allobs_df)
print(sprintf("Found %d duplicates by all features:", nrow(dup_allobs_df)))## [1] "Found 304 duplicates by all features:"myprint_df(dup_allobs_df)## description biddable startprice condition cellular
## 1711 1 0.99 For parts or not working Unknown
## 2608 1 0.99 For parts or not working Unknown
## 293 1 5.00 Used Unknown
## 478 1 5.00 Used Unknown
## 385 0 15.00 Used 0
## 390 0 15.00 Used 0
## carrier color storage productline sold UniqueID .src
## 1711 Unknown Unknown 16 Unknown 1 11711 Train
## 2608 Unknown Unknown 16 Unknown NA 12608 Test
## 293 Unknown White 16 Unknown 1 10293 Train
## 478 Unknown White 16 Unknown 1 10478 Train
## 385 None Black 16 Unknown 0 10385 Train
## 390 None Black 16 Unknown 0 10390 Train
## description biddable startprice condition cellular
## 1956 1 0.99 Used 0
## 828 1 249.97 Manufacturer refurbished 1
## 3 0 199.99 Used 0
## 1649 0 209.00 For parts or not working Unknown
## 2111 1 200.00 Used 0
## 172 0 269.00 Used 0
## carrier color storage productline sold UniqueID .src
## 1956 None Unknown 16 iPad 2 NA 11956 Test
## 828 Unknown Black 64 iPad 2 0 10828 Train
## 3 None White 16 iPad 4 1 10003 Train
## 1649 Unknown Unknown 16 iPad Air 0 11649 Train
## 2111 None Space Gray 64 iPad mini 2 NA 12111 Test
## 172 None Unknown 32 iPad mini 2 0 10172 Train
## description biddable startprice condition cellular carrier color
## 8 0 329.99 New 0 None White
## 660 0 329.99 New 0 None White
## 319 0 345.00 New 0 None Gold
## 1886 0 345.00 New 0 None Gold
## 1363 0 498.88 New 1 Verizon Gold
## 1394 0 498.88 New 1 Verizon Gold
## storage productline sold UniqueID .src
## 8 16 iPad mini 3 0 10008 Train
## 660 16 iPad mini 3 0 10660 Train
## 319 16 iPad mini 3 1 10319 Train
## 1886 16 iPad mini 3 NA 11886 Test
## 1363 16 iPad mini 3 0 11363 Train
## 1394 16 iPad mini 3 0 11394 Train# print(dup_allobs_df[, c(glb_id_var, glb_rsp_var_raw,
# "description", "startprice", "biddable")])
# write.csv(dup_allobs_df[, c("UniqueID"), FALSE], "ebayipads_dups.csv", row.names=FALSE)
dupobs_df <- tidyr::unite(dup_allobs_df, "allfeats", -c(sold, UniqueID, .src), sep="#")
# dupobs_df <- dplyr::group_by(dupobs_df, allfeats)
# dupobs_df <- dupobs_df[, "UniqueID", FALSE]
# dupobs_df <- ungroup(dupobs_df)
#
# dupobs_df$.rownames <- row.names(dupobs_df)
grpobs_df <- data.frame(allfeats=unique(dupobs_df[, "allfeats"]))
grpobs_df$.grpid <- row.names(grpobs_df)
dupobs_df <- merge(dupobs_df, grpobs_df)
# dupobs_tbl <- table(dupobs_df$.grpid)
# print(max(dupobs_tbl))
# print(dupobs_tbl[which.max(dupobs_tbl)])
# print(dupobs_df[dupobs_df$.grpid == names(dupobs_tbl[which.max(dupobs_tbl)]), ])
# print(dupobs_df[dupobs_df$.grpid == 106, ])
# for (grpid in c(9, 17, 31, 36, 53))
# print(dupobs_df[dupobs_df$.grpid == grpid, ])
dupgrps_df <- as.data.frame(table(dupobs_df$.grpid, dupobs_df$sold, useNA="ifany"))
names(dupgrps_df)[c(1,2)] <- c(".grpid", "sold")
dupgrps_df$.grpid <- as.numeric(as.character(dupgrps_df$.grpid))
dupgrps_df <- tidyr::spread(dupgrps_df, sold, Freq)
names(dupgrps_df)[-1] <- paste("sold", names(dupgrps_df)[-1], sep=".")
dupgrps_df$.freq <- sapply(1:nrow(dupgrps_df), function(row) sum(dupgrps_df[row, -1]))
myprint_df(orderBy(~-.freq, dupgrps_df))## .grpid sold.0 sold.1 sold.NA .freq
## 40 40 0 6 3 9
## 106 106 0 4 1 5
## 9 9 0 1 3 4
## 17 17 0 3 1 4
## 36 36 0 3 1 4
## 53 53 0 2 2 4
## .grpid sold.0 sold.1 sold.NA .freq
## 10 10 0 2 0 2
## 42 42 0 1 1 2
## 57 57 1 0 1 2
## 66 66 1 0 1 2
## 91 91 0 1 1 2
## 101 101 0 1 1 2
## .grpid sold.0 sold.1 sold.NA .freq
## 130 130 1 0 1 2
## 131 131 1 1 0 2
## 132 132 0 1 1 2
## 133 133 2 0 0 2
## 134 134 0 1 1 2
## 135 135 2 0 0 2print("sold Conflicts:")## [1] "sold Conflicts:"print(subset(dupgrps_df, (sold.0 > 0) & (sold.1 > 0)))## .grpid sold.0 sold.1 sold.NA .freq
## 4 4 1 1 0 2
## 22 22 1 1 0 2
## 23 23 1 1 0 2
## 74 74 1 1 0 2
## 83 83 1 1 0 2
## 84 84 1 1 0 2
## 95 95 1 1 0 2
## 102 102 1 1 0 2
## 109 109 1 1 0 2
## 111 111 1 1 0 2
## 122 122 1 1 0 2
## 131 131 1 1 0 2#dupobs_df[dupobs_df$.grpid == 4, ]
if (nrow(subset(dupgrps_df, (sold.0 > 0) & (sold.1 > 0) & (sold.0 != sold.1))) > 0)
stop("Duplicate conflicts are resolvable")
print("Test & Train Groups:")## [1] "Test & Train Groups:"print(subset(dupgrps_df, (sold.NA > 0)))## .grpid sold.0 sold.1 sold.NA .freq
## 1 1 0 1 1 2
## 5 5 1 0 1 2
## 7 7 0 0 2 2
## 8 8 1 0 1 2
## 9 9 0 1 3 4
## 12 12 0 0 2 2
## 14 14 0 1 1 2
## 15 15 0 0 2 2
## 17 17 0 3 1 4
## 18 18 0 2 1 3
## 19 19 0 2 1 3
## 24 24 0 2 1 3
## 26 26 1 0 1 2
## 28 28 1 0 1 2
## 30 30 0 1 1 2
## 32 32 0 0 2 2
## 33 33 0 1 1 2
## 35 35 0 2 1 3
## 36 36 0 3 1 4
## 37 37 0 0 2 2
## 38 38 0 1 1 2
## 40 40 0 6 3 9
## 41 41 0 0 2 2
## 42 42 0 1 1 2
## 43 43 0 1 1 2
## 44 44 0 2 1 3
## 47 47 0 1 1 2
## 48 48 0 0 2 2
## 49 49 0 1 2 3
## 51 51 0 1 1 2
## 53 53 0 2 2 4
## 54 54 0 1 1 2
## 55 55 1 0 2 3
## 56 56 1 0 1 2
## 57 57 1 0 1 2
## 58 58 0 0 2 2
## 59 59 1 0 1 2
## 60 60 1 0 1 2
## 63 63 0 1 1 2
## 66 66 1 0 1 2
## 67 67 1 0 1 2
## 68 68 0 0 2 2
## 69 69 1 0 1 2
## 73 73 0 1 1 2
## 76 76 0 2 1 3
## 86 86 0 0 2 2
## 87 87 1 0 1 2
## 89 89 1 0 1 2
## 90 90 0 0 2 2
## 91 91 0 1 1 2
## 93 93 0 1 1 2
## 94 94 1 0 1 2
## 99 99 0 1 1 2
## 101 101 0 1 1 2
## 103 103 0 1 1 2
## 104 104 1 0 1 2
## 106 106 0 4 1 5
## 107 107 0 1 1 2
## 108 108 0 1 1 2
## 112 112 1 0 1 2
## 114 114 0 1 1 2
## 115 115 0 1 1 2
## 116 116 1 0 1 2
## 117 117 0 2 1 3
## 118 118 0 1 1 2
## 121 121 1 0 1 2
## 124 124 1 0 1 2
## 128 128 0 1 1 2
## 130 130 1 0 1 2
## 132 132 0 1 1 2
## 134 134 0 1 1 2glb_allobs_df <- merge(glb_allobs_df, dupobs_df[, c(glb_id_var, ".grpid")],
by=glb_id_var, all.x=TRUE)
glb_exclude_vars_as_features <- c(".grpid", glb_exclude_vars_as_features)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "inspect.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 8.739 11.506 2.768
## 2 inspect.data 2 0 11.507 NA NAStep 2.0: inspect data
#print(str(glb_allobs_df))
#View(glb_allobs_df)
dsp_class_dstrb <- function(var) {
xtab_df <- mycreate_xtab_df(glb_allobs_df, c(".src", var))
rownames(xtab_df) <- xtab_df$.src
xtab_df <- subset(xtab_df, select=-.src)
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Performed repeatedly in other chunks
glb_chk_data <- function() {
# Histogram of predictor in glb_trnobs_df & glb_newobs_df
print(myplot_histogram(glb_allobs_df, glb_rsp_var_raw) + facet_wrap(~ .src))
if (glb_is_classification)
dsp_class_dstrb(var=ifelse(glb_rsp_var %in% names(glb_allobs_df),
glb_rsp_var, glb_rsp_var_raw))
mycheck_problem_data(glb_allobs_df)
}
glb_chk_data()## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## [1] "numeric data missing in : "
## sold
## 798
## [1] "numeric data w/ 0s in : "
## biddable sold
## 1444 999
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid
## 0 NA# Create new features that help diagnostics
if (!is.null(glb_map_rsp_raw_to_var)) {
glb_allobs_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_allobs_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_allobs_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
if (glb_is_classification) dsp_class_dstrb(glb_rsp_var)
}
# check distribution of all numeric data
dsp_numeric_feats_dstrb <- function(feats_vctr) {
for (feat in feats_vctr) {
print(sprintf("feat: %s", feat))
if (glb_is_regression)
gp <- myplot_scatter(df=glb_allobs_df, ycol_name=glb_rsp_var, xcol_name=feat,
smooth=TRUE)
if (glb_is_classification)
gp <- myplot_box(df=glb_allobs_df, ycol_names=feat, xcol_name=glb_rsp_var)
if (inherits(glb_allobs_df[, feat], "factor"))
gp <- gp + facet_wrap(reformulate(feat))
print(gp)
}
}
# dsp_numeric_vars_dstrb(setdiff(names(glb_allobs_df),
# union(myfind_chr_cols_df(glb_allobs_df),
# c(glb_rsp_var_raw, glb_rsp_var))))
add_new_diag_feats <- function(obs_df, ref_df=glb_allobs_df) {
require(plyr)
obs_df <- mutate(obs_df,
# <col_name>.NA=is.na(<col_name>),
# <col_name>.fctr=factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# <col_name>.fctr=relevel(factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# "<ref_val>"),
# <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")),
# as.factor(c("R", "<ref_val>")),
# ref="<ref_val>"),
# This doesn't work - use sapply instead
# <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)),
#
# Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
# Year=year(Date.my),
# Month=months(Date.my),
# Weekday=weekdays(Date.my)
# <col_name>=<table>[as.character(<col2_name>)],
# <col_name>=as.numeric(<col2_name>),
# <col_name> = trunc(<col2_name> / 100),
.rnorm = rnorm(n=nrow(obs_df))
)
# If levels of a factor are different across obs_df & glb_newobs_df; predict.glm fails
# Transformations not handled by mutate
# obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df),
# function(row_ix) grep(obs_df[row_ix, "<col_name>"],
# levels(obs_df[row_ix, "<col_name>.fctr"])))
#print(summary(obs_df))
#print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
return(obs_df)
}
glb_allobs_df <- add_new_diag_feats(glb_allobs_df)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following objects are masked from 'package:gdata':
##
## combine, first, last
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Merge some <descriptor>
# glb_allobs_df$<descriptor>.my <- glb_allobs_df$<descriptor>
# glb_allobs_df[grepl("\\bAIRPORT\\b", glb_allobs_df$<descriptor>.my),
# "<descriptor>.my"] <- "AIRPORT"
# glb_allobs_df$<descriptor>.my <-
# plyr::revalue(glb_allobs_df$<descriptor>.my, c(
# "ABANDONED BUILDING" = "OTHER",
# "##" = "##"
# ))
# print(<descriptor>_freq_df <- mycreate_sqlxtab_df(glb_allobs_df, c("<descriptor>.my")))
# # print(dplyr::filter(<descriptor>_freq_df, grepl("(MEDICAL|DENTAL|OFFICE)", <descriptor>.my)))
# # print(dplyr::filter(dplyr::select(glb_allobs_df, -<var.zoo>),
# # grepl("STORE", <descriptor>.my)))
# glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, "<descriptor>")
# Check distributions of newly transformed / extracted vars
# Enhancement: remove vars that were displayed ealier
dsp_numeric_feats_dstrb(feats_vctr=setdiff(names(glb_allobs_df),
c(myfind_chr_cols_df(glb_allobs_df), glb_rsp_var_raw, glb_rsp_var,
glb_exclude_vars_as_features)))## [1] "feat: biddable"## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## [1] "feat: .rnorm"## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
# Convert factors to dummy variables
# Build splines require(splines); bsBasis <- bs(training$age, df=3)
#pairs(subset(glb_trnobs_df, select=-c(col_symbol)))
# Check for glb_newobs_df & glb_trnobs_df features range mismatches
# Other diagnostics:
# print(subset(glb_trnobs_df, <col1_name> == max(glb_trnobs_df$<col1_name>, na.rm=TRUE) &
# <col2_name> <= mean(glb_trnobs_df$<col1_name>, na.rm=TRUE)))
# print(glb_trnobs_df[which.max(glb_trnobs_df$<col_name>),])
# print(<col_name>_freq_glb_trnobs_df <- mycreate_tbl_df(glb_trnobs_df, "<col_name>"))
# print(which.min(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>)[, 2]))
# print(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>))
# print(table(is.na(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(table(sign(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(mycreate_xtab_df(glb_trnobs_df, <col1_name>))
# print(mycreate_xtab_df(glb_trnobs_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mycreate_xtab_df(glb_trnobs_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnobs_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnobs_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mutate(<col1_name>_<col2_name>_xtab_glb_trnobs_df,
# <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>)))
# print(mycreate_sqlxtab_df(glb_allobs_df, c("<col1_name>", "<col2_name>")))
# print(<col2_name>_min_entity_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>.NA, glb_trnobs_df$<col2_name>, mean, na.rm=TRUE)))
# Other plots:
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnobs_df, Symbol %in% c("CocaCola", "ProcterGamble")),
# "Date.POSIX", "StockPrice", facet_row_colnames="Symbol") +
# geom_vline(xintercept=as.numeric(as.POSIXlt("2003-03-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1983-01-01")))
# )
# print(myplot_line(subset(glb_trnobs_df, Date.POSIX > as.POSIXct("2004-01-01")),
# "Date.POSIX", "StockPrice") +
# geom_line(aes(color=Symbol)) +
# coord_cartesian(xlim=c(as.POSIXct("1990-01-01"),
# as.POSIXct("2000-01-01"))) +
# coord_cartesian(ylim=c(0, 250)) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-09-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-11-01")))
# )
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") +
# geom_point(data=subset(glb_allobs_df, <condition>),
# mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5) +
# geom_vline(xintercept=84))
glb_chunks_df <- myadd_chunk(glb_chunks_df, "scrub.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 2 inspect.data 2 0 11.507 13.552 2.045
## 3 scrub.data 2 1 13.553 NA NAStep 2.1: scrub data
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## sold
## 798
## [1] "numeric data w/ 0s in : "
## biddable sold
## 1444 999
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid
## 0 NAfindOffendingCharacter <- function(x, maxStringLength=256){
print(x)
for (c in 1:maxStringLength){
offendingChar <- substr(x,c,c)
#print(offendingChar) #uncomment if you want the indiv characters printed
#the next character is the offending multibyte Character
}
}
# string_vector <- c("test", "Se\x96ora", "works fine")
# lapply(string_vector, findOffendingCharacter)
# lapply(glb_allobs_df$description[29], findOffendingCharacter)
sel_obs <- function(vars_lst, ignore.case=TRUE, perl=FALSE) {
tmp_df <- glb_allobs_df
# Does not work for Popular == NAs ???
# if (!is.null(Popular)) {
# if (is.na(Popular))
# tmp_df <- tmp_df[is.na(tmp_df$Popular), ] else
# tmp_df <- tmp_df[tmp_df$Popular == Popular, ]
# }
# if (!is.null(NewsDesk))
# tmp_df <- tmp_df[tmp_df$NewsDesk == NewsDesk, ]
for (var in names(vars_lst)) {
if (grepl(".contains", var))
tmp_df <- tmp_df[grep(vars_lst[var],
tmp_df[, unlist(strsplit(var, ".contains"))],
ignore.case=ignore.case, perl=perl), ]
else
tmp_df <- tmp_df[tmp_df[, var] == vars_lst[var], ]
}
return(glb_allobs_df[, glb_id_var] %in% tmp_df[, glb_id_var])
}
#print(glb_allobs_df[sel_obs(list(description.contains="mini(?!m)"), perl=TRUE), "description"])
dsp_obs <- function(..., cols=c(NULL), all=FALSE) {
tmp_df <- glb_allobs_df[sel_obs(...),
c(glb_id_var, glb_rsp_var, glb_category_var, glb_txt_vars, cols),
FALSE]
if(all) { print(tmp_df) } else { myprint_df(tmp_df) }
}
#dsp_obs(list(description.contains="mini(?!m)"), perl=TRUE)
#dsp_obs(Popular=1, NewsDesk="", SectionName="", Headline.contains="Boehner")
# dsp_obs(Popular=1, NewsDesk="", SectionName="")
# dsp_obs(Popular=NA, NewsDesk="", SectionName="")
dsp_hdlxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "Headline", glb_rsp_var)))
#dsp_hdlxtab("(1914)|(1939)")
dsp_catxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# dsp_catxtab("1914)|(1939)")
# dsp_catxtab("19(14|39|64):")
# dsp_catxtab("19..:")
# Merge some categories
# glb_allobs_df$myCategory <-
# plyr::revalue(glb_allobs_df$myCategory, c(
# "#Business Day#Dealbook" = "Business#Business Day#Dealbook",
# "#Business Day#Small Business" = "Business#Business Day#Small Business",
# "dummy" = "dummy"
# ))
# ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
# mycreate_sqlxtab_df(glb_allobs_df,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# myprint_df(ctgry_xtab_df)
# write.table(ctgry_xtab_df, paste0(glb_out_pfx, "ctgry_xtab.csv"),
# row.names=FALSE)
# ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
# myCategory + NewsDesk + SectionName + SubsectionName ~
# Popular.fctr, sum, value.var=".n"))
# myprint_df(ctgry_cast_df)
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_cast.csv"),
# row.names=FALSE)
# print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df[, glb_rsp_var],
# useNA="ifany"))
dsp_chisq.test <- function(...) {
sel_df <- glb_allobs_df[sel_obs(...) &
!is.na(glb_allobs_df$Popular), ]
sel_df$.marker <- 1
ref_df <- glb_allobs_df[!is.na(glb_allobs_df$Popular), ]
mrg_df <- merge(ref_df[, c(glb_id_var, "Popular")],
sel_df[, c(glb_id_var, ".marker")], all.x=TRUE)
mrg_df[is.na(mrg_df)] <- 0
print(mrg_tbl <- table(mrg_df$.marker, mrg_df$Popular))
print("Rows:Selected; Cols:Popular")
#print(mrg_tbl)
print(chisq.test(mrg_tbl))
}
# dsp_chisq.test(Headline.contains="[Ee]bola")
# dsp_chisq.test(Snippet.contains="[Ee]bola")
# dsp_chisq.test(Abstract.contains="[Ee]bola")
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola"), ],
# c(glb_rsp_var, "NewsDesk", "SectionName", "SubsectionName")))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName))
# print(table(glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# glb_allobs_df$myCategory.fctr <- as.factor(glb_allobs_df$myCategory)
# glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName"))
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))##
## AT&T None Other Sprint T-Mobile Unknown Verizon
## 0 0 1593 0 0 0 0 0
## 1 288 0 4 36 28 172 196
## Unknown 4 4 2 0 0 330 0# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
# (glb_allobs_df$carrier %in% c("AT&T", "Other")),
# c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
(glb_allobs_df$carrier %in% c("AT&T", "Other")),
"cellular"] <- "1"
# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
# (glb_allobs_df$carrier %in% c("None")),
# c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
(glb_allobs_df$carrier %in% c("None")),
"cellular"] <- "0"
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))##
## AT&T None Other Sprint T-Mobile Unknown Verizon
## 0 0 1597 0 0 0 0 0
## 1 292 0 6 36 28 172 196
## Unknown 0 0 0 0 0 330 0Step 2.1: scrub data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "transform.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 3 scrub.data 2 1 13.553 14.205 0.652
## 4 transform.data 2 2 14.206 NA NA### Mapping dictionary
#sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_map_vars)) {
for (feat in glb_map_vars) {
map_df <- myimport_data(url=glb_map_urls[[feat]],
comment="map_df",
print_diagn=TRUE)
glb_allobs_df <- mymap_codes(glb_allobs_df, feat, names(map_df)[2],
map_df, map_join_col_name=names(map_df)[1],
map_tgt_col_name=names(map_df)[2])
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_map_vars)
}
### Forced Assignments
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (feat in glb_assign_vars) {
new_feat <- paste0(feat, ".my")
print(sprintf("Forced Assignments for: %s -> %s...", feat, new_feat))
glb_allobs_df[, new_feat] <- glb_allobs_df[, feat]
pairs <- glb_assign_pairs_lst[[feat]]
for (pair_ix in 1:length(pairs$from)) {
if (is.na(pairs$from[pair_ix]))
nobs <- nrow(filter(glb_allobs_df,
is.na(eval(parse(text=feat),
envir=glb_allobs_df)))) else
nobs <- sum(glb_allobs_df[, feat] == pairs$from[pair_ix])
#nobs <- nrow(filter(glb_allobs_df, is.na(Married.fctr))) ; print(nobs)
if ((is.na(pairs$from[pair_ix])) && (is.na(pairs$to[pair_ix])))
stop("what are you trying to do ???")
if (is.na(pairs$from[pair_ix]))
glb_allobs_df[is.na(glb_allobs_df[, feat]), new_feat] <-
pairs$to[pair_ix] else
glb_allobs_df[glb_allobs_df[, feat] == pairs$from[pair_ix], new_feat] <-
pairs$to[pair_ix]
print(sprintf(" %s -> %s for %s obs",
pairs$from[pair_ix], pairs$to[pair_ix], format(nobs, big.mark=",")))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_assign_vars)
}
### Derivations using mapping functions
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (new_feat in glb_derive_vars) {
print(sprintf("Creating new feature: %s...", new_feat))
args_lst <- NULL
for (arg in glb_derive_lst[[new_feat]]$args)
args_lst[[arg]] <- glb_allobs_df[, arg]
glb_allobs_df[, new_feat] <- do.call(glb_derive_lst[[new_feat]]$mapfn, args_lst)
}## [1] "Creating new feature: idseq.my..."
## [1] "Creating new feature: prdline.my..."
## [1] "Creating new feature: startprice.log..."
## [1] "Creating new feature: descr.my..."#stop(here")
#hex_vctr <- c("\n", "\211", "\235", "\317", "\333")
hex_regex <- paste0(c("\n", "\211", "\235", "\317", "\333"), collapse="|")
for (obs_id in c(10178, 10948, 11514, 11904, 12157, 12210, 12659)) {
# tmp_str <- unlist(strsplit(glb_allobs_df[row_pos, "descr.my"], ""))
# glb_allobs_df[row_pos, "descr.my"] <- paste0(tmp_str[!tmp_str %in% hex_vctr],
# collapse="")
row_pos <- which(glb_allobs_df$UniqueID == obs_id)
glb_allobs_df[row_pos, "descr.my"] <-
gsub(hex_regex, " ", glb_allobs_df[row_pos, "descr.my"])
}Step 2.2: transform data
#```{r extract_features, cache=FALSE, eval=!is.null(glb_txt_vars)}
glb_chunks_df <- myadd_chunk(glb_chunks_df, "extract.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 4 transform.data 2 2 14.206 14.715 0.509
## 5 extract.features 3 0 14.715 NA NAextract.features_chunk_df <- myadd_chunk(NULL, "extract.features_bgn")## label step_major step_minor bgn end elapsed
## 1 extract.features_bgn 1 0 14.721 NA NA# Options:
# Select Tf, log(1 + Tf), Tf-IDF or BM25Tf-IDf
# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnobs_df$<col_name>), -2, na.pad=TRUE)
# glb_trnobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newobs_df$<col_name>), -2, na.pad=TRUE)
# glb_newobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
#
# glb_newobs_df[1, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df) - 1,
# "<col_name>"]
# glb_newobs_df[2, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df),
# "<col_name>"]
# glb_allobs_df <- mutate(glb_allobs_df,
# A.P.http=ifelse(grepl("http",Added,fixed=TRUE), 1, 0)
# )
#
# glb_trnobs_df <- mutate(glb_trnobs_df,
# )
#
# glb_newobs_df <- mutate(glb_newobs_df,
# )
# Convert dates to numbers
# typically, dates come in as chars;
# so this must be done before converting chars to factors
#stop(here"); sav_allobs_df <- glb_allobs_df #; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_date_vars)) {
glb_allobs_df <- cbind(glb_allobs_df,
myextract_dates_df(df=glb_allobs_df, vars=glb_date_vars,
id_vars=glb_id_var, rsp_var=glb_rsp_var))
for (sfx in c("", ".POSIX"))
glb_exclude_vars_as_features <-
union(glb_exclude_vars_as_features,
paste(glb_date_vars, sfx, sep=""))
for (feat in glb_date_vars) {
glb_allobs_df <- orderBy(reformulate(paste0(feat, ".POSIX")), glb_allobs_df)
# print(myplot_scatter(glb_allobs_df, xcol_name=paste0(feat, ".POSIX"),
# ycol_name=glb_rsp_var, colorcol_name=glb_rsp_var))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >=
strptime("2012-12-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".POSIX"),
ycol_name=glb_rsp_var, colorcol_name=paste0(feat, ".wkend")))
# Create features that measure the gap between previous timestamp in the data
require(zoo)
z <- zoo(as.numeric(as.POSIXlt(glb_allobs_df[, paste0(feat, ".POSIX")])))
glb_allobs_df[, paste0(feat, ".zoo")] <- z
print(head(glb_allobs_df[, c(glb_id_var, feat, paste0(feat, ".zoo"))]))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >
strptime("2012-10-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".zoo"), ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var))
b <- zoo(, seq(nrow(glb_allobs_df)))
last1 <- as.numeric(merge(z-lag(z, -1), b, all=TRUE)); last1[is.na(last1)] <- 0
glb_allobs_df[, paste0(feat, ".last1.log")] <- log(1 + last1)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last1.log")] > 0, ],
ycol_names=paste0(feat, ".last1.log"),
xcol_name=glb_rsp_var))
last2 <- as.numeric(merge(z-lag(z, -2), b, all=TRUE)); last2[is.na(last2)] <- 0
glb_allobs_df[, paste0(feat, ".last2.log")] <- log(1 + last2)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last2.log")] > 0, ],
ycol_names=paste0(feat, ".last2.log"),
xcol_name=glb_rsp_var))
last10 <- as.numeric(merge(z-lag(z, -10), b, all=TRUE)); last10[is.na(last10)] <- 0
glb_allobs_df[, paste0(feat, ".last10.log")] <- log(1 + last10)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last10.log")] > 0, ],
ycol_names=paste0(feat, ".last10.log"),
xcol_name=glb_rsp_var))
last100 <- as.numeric(merge(z-lag(z, -100), b, all=TRUE)); last100[is.na(last100)] <- 0
glb_allobs_df[, paste0(feat, ".last100.log")] <- log(1 + last100)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last100.log")] > 0, ],
ycol_names=paste0(feat, ".last100.log"),
xcol_name=glb_rsp_var))
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(paste0(feat, ".zoo")))
# all2$last3 = as.numeric(merge(z-lag(z, -3), b, all = TRUE))
# all2$last5 = as.numeric(merge(z-lag(z, -5), b, all = TRUE))
# all2$last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
# all2$last20 = as.numeric(merge(z-lag(z, -20), b, all = TRUE))
# all2$last50 = as.numeric(merge(z-lag(z, -50), b, all = TRUE))
#
#
# # order table
# all2 = all2[order(all2$id),]
#
# ## fill in NAs
# # count averages
# na.avg = all2 %>% group_by(weekend, hour) %>% dplyr::summarise(
# last1=mean(last1, na.rm=TRUE),
# last3=mean(last3, na.rm=TRUE),
# last5=mean(last5, na.rm=TRUE),
# last10=mean(last10, na.rm=TRUE),
# last20=mean(last20, na.rm=TRUE),
# last50=mean(last50, na.rm=TRUE)
# )
#
# # fill in averages
# na.merge = merge(all2, na.avg, by=c("weekend","hour"))
# na.merge = na.merge[order(na.merge$id),]
# for(i in c("last1", "last3", "last5", "last10", "last20", "last50")) {
# y = paste0(i, ".y")
# idx = is.na(all2[[i]])
# all2[idx,][[i]] <- na.merge[idx,][[y]]
# }
# rm(na.avg, na.merge, b, i, idx, n, pd, sec, sh, y, z)
}
}
rm(last1, last10, last100)## Warning in rm(last1, last10, last100): object 'last1' not found## Warning in rm(last1, last10, last100): object 'last10' not found## Warning in rm(last1, last10, last100): object 'last100' not found# Create factors of string variables
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "factorize.str.vars"), major.inc=TRUE)## label step_major step_minor bgn end
## 1 extract.features_bgn 1 0 14.721 14.733
## 2 extract.features_factorize.str.vars 2 0 14.734 NA
## elapsed
## 1 0.012
## 2 NA#stop(here"); sav_allobs_df <- glb_allobs_df; #glb_allobs_df <- sav_allobs_df
print(str_vars <- myfind_chr_cols_df(glb_allobs_df))## description condition cellular carrier color
## "description" "condition" "cellular" "carrier" "color"
## storage productline .src .grpid prdline.my
## "storage" "productline" ".src" ".grpid" "prdline.my"
## descr.my
## "descr.my"if (length(str_vars <- setdiff(str_vars,
c(glb_exclude_vars_as_features, glb_txt_vars))) > 0) {
for (var in str_vars) {
warning("Creating factors of string variable: ", var,
": # of unique values: ", length(unique(glb_allobs_df[, var])))
glb_allobs_df[, paste0(var, ".fctr")] <-
relevel(factor(glb_allobs_df[, var]),
names(which.max(table(glb_allobs_df[, var], useNA = "ifany"))))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, str_vars)
}## Warning: Creating factors of string variable: condition: # of unique
## values: 6## Warning: Creating factors of string variable: cellular: # of unique values:
## 3## Warning: Creating factors of string variable: carrier: # of unique values:
## 7## Warning: Creating factors of string variable: color: # of unique values: 5## Warning: Creating factors of string variable: storage: # of unique values:
## 5## Warning: Creating factors of string variable: prdline.my: # of unique
## values: 12if (!is.null(glb_txt_vars)) {
require(foreach)
require(gsubfn)
require(stringr)
require(tm)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text"), major.inc=TRUE)
chk_pattern_freq <- function(rex_str, ignore.case=TRUE) {
match_mtrx <- str_extract_all(txt_vctr, regex(rex_str, ignore_case=ignore.case),
simplify=TRUE)
match_df <- as.data.frame(match_mtrx[match_mtrx != ""])
names(match_df) <- "pattern"
return(mycreate_sqlxtab_df(match_df, "pattern"))
}
# match_lst <- gregexpr("\\bok(?!ay)", txt_vctr[746], ignore.case = FALSE, perl=TRUE); print(match_lst)
dsp_pattern <- function(rex_str, ignore.case=TRUE, print.all=TRUE) {
match_lst <- gregexpr(rex_str, txt_vctr, ignore.case = ignore.case, perl=TRUE)
match_lst <- regmatches(txt_vctr, match_lst)
match_df <- data.frame(matches=sapply(match_lst,
function (elems) paste(elems, collapse="#")))
match_df <- subset(match_df, matches != "")
if (print.all)
print(match_df)
return(match_df)
}
dsp_matches <- function(rex_str, ix) {
print(match_pos <- gregexpr(rex_str, txt_vctr[ix], perl=TRUE))
print(str_sub(txt_vctr[ix], (match_pos[[1]] / 100) * 99 + 0,
(match_pos[[1]] / 100) * 100 + 100))
}
myapply_gsub <- function(...) {
if ((length_lst <- length(names(gsub_map_lst))) == 0)
return(txt_vctr)
for (ptn_ix in 1:length_lst) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
length(names(gsub_map_lst)), names(gsub_map_lst)[ptn_ix]))
txt_vctr <- gsub(names(gsub_map_lst)[ptn_ix], gsub_map_lst[[ptn_ix]],
txt_vctr, ...)
}
return(txt_vctr)
}
myapply_txtmap <- function(txt_vctr, ...) {
nrows <- nrow(glb_txt_map_df)
for (ptn_ix in 1:nrows) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
nrows, glb_txt_map_df[ptn_ix, "rex_str"]))
txt_vctr <- gsub(glb_txt_map_df[ptn_ix, "rex_str"],
glb_txt_map_df[ptn_ix, "rpl_str"],
txt_vctr, ...)
}
return(txt_vctr)
}
chk.equal <- function(bgn, end) {
print(all.equal(sav_txt_lst[["Headline"]][bgn:end],
glb_txt_lst[["Headline"]][bgn:end]))
}
dsp.equal <- function(bgn, end) {
print(sav_txt_lst[["Headline"]][bgn:end])
print(glb_txt_lst[["Headline"]][bgn:end])
}
#sav_txt_lst <- glb_txt_lst; all.equal(sav_txt_lst, glb_txt_lst)
#all.equal(sav_txt_lst[["Headline"]][1:4200], glb_txt_lst[["Headline"]][1:4200])
#chk.equal( 1, 100)
#dsp.equal(86, 90)
txt_map_filename <- paste0(glb_txt_munge_filenames_pfx, "map.csv")
if (!file.exists(txt_map_filename))
stop(txt_map_filename, " not found!")
glb_txt_map_df <- read.csv(txt_map_filename, comment.char="#", strip.white=TRUE)
glb_txt_lst <- list();
print(sprintf("Building glb_txt_lst..."))
glb_txt_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_vctr <- glb_allobs_df[, txt_var]
# myapply_txtmap shd be created as a tm_map::content_transformer ?
#print(glb_txt_map_df)
#txt_var=glb_txt_vars[3]; txt_vctr <- glb_txt_lst[[txt_var]]
#print(rex_str <- glb_txt_map_df[3, "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rex_str == "\\bWall St\\.", "rex_str"])
#print(rex_str <- glb_txt_map_df[grepl("du Pont", glb_txt_map_df$rex_str), "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rpl_str == "versus", "rex_str"])
#print(tmp_vctr <- grep(rex_str, txt_vctr, value=TRUE, ignore.case=FALSE))
#ret_lst <- regexec(rex_str, txt_vctr, ignore.case=FALSE); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
#gsub(rex_str, glb_txt_map_df[glb_txt_map_df$rex_str == rex_str, "rpl_str"], tmp_vctr, ignore.case=FALSE)
#grep("Hong Hong", txt_vctr, value=TRUE)
txt_vctr <- myapply_txtmap(txt_vctr, ignore.case=FALSE)
}
names(glb_txt_lst) <- glb_txt_vars
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining OK in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "(?<!(BO|HO|LO))OK(?!(E\\!|ED|IE|IN|S ))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "Ok(?!(a\\.|ay|in|ra|um))", ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "(?<!( b| B| c| C| g| G| j| M| p| P| w| W| r| Z|\\(b|ar|bo|Bo|co|Co|Ew|gk|go|ho|ig|jo|kb|ke|Ke|ki|lo|Lo|mo|mt|no|No|po|ra|ro|sm|Sm|Sp|to|To))ok(?!(ay|bo|e |e\\)|e,|e\\.|eb|ed|el|en|er|es|ey|i |ie|in|it|ka|ke|ki|ly|on|oy|ra|st|u |uc|uy|yl|yo))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
}
# txt_vctr <- glb_txt_lst[[glb_txt_vars[1]]]
# print(chk_pattern_freq(rex_str <- "(?<!( b| c| C| p|\\(b|bo|co|lo|Lo|Sp|to|To))ok(?!(ay|e |e\\)|e,|e\\.|ed|el|en|es|ey|ie|in|on|ra))", ignore.case=FALSE))
# print(chk_pattern_freq(rex_str <- "ok(?!(ay|el|on|ra))", ignore.case=FALSE))
# dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
# dsp_matches(rex_str, ix=8)
# substr(txt_vctr[86], 5613, 5620)
# substr(glb_allobs_df[301, "review"], 550, 650)
#stop(here"); sav_txt_lst <- glb_txt_lst
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining Acronyms in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "([[:upper:]]\\.( *)){2,}", ignore.case=FALSE))
# Check for names
print(subset(chk_pattern_freq(rex_str <- "(([[:upper:]]+)\\.( *)){1}",
ignore.case=FALSE),
.n > 1))
# dsp_pattern(rex_str="(OK\\.( *)){1}", ignore.case=FALSE)
# dsp_matches(rex_str="(OK\\.( *)){1}", ix=557)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)(\\B)", ix=461)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)", ix=461)
#print(str_sub(txt_vctr[676], 10100, 10200))
#print(str_sub(txt_vctr[74], 1, -1))
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl("( |-)[[:upper:]]", pattern))))
print(" consider cleaning if relevant to problem domain; geography name; .n > 1")
#grep("New G", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Wins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
#stop(here"); sav_txt_lst <- glb_txt_lst
for (txt_var in glb_txt_vars) {
re_str <- "\\b(N|S|E|W|C)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("N Weaver", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(North|South|East|West|Central)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
if (nrow(filtered_df <- subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))) > 0)
print(orderBy(~ -.n +pattern, filtered_df))
#grep("Central (African|Bankers|Cast|Italy|Role|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("East (Africa|Berlin|London|Poland|Rivals|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("North (American|Korean|West)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("South (Pacific|Street)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Martins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
find_cmpnd_wrds <- function(txt_vctr) {
txt_corpus <- Corpus(VectorSource(txt_vctr))
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy=TRUE)
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy=TRUE)
txt_corpus <- tm_map(txt_corpus, removePunctuation, lazy=TRUE,
preserve_intra_word_dashes=TRUE, lazy=TRUE)
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full, full_Tf_df)
cmpnd_Tf_df <- full_Tf_df[grep("-", full_Tf_df$term, value=TRUE) ,]
txt_compound_filename <- paste0(glb_txt_munge_filenames_pfx, "compound.csv")
if (!file.exists(txt_compound_filename))
stop(txt_compound_filename, " not found!")
filter_df <- read.csv(txt_compound_filename, comment.char="#", strip.white=TRUE)
cmpnd_Tf_df$filter <- FALSE
for (row_ix in 1:nrow(filter_df))
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "filter"] <-
grepl(filter_df[row_ix, "rex_str"],
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "term"], ignore.case=TRUE)
cmpnd_Tf_df <- subset(cmpnd_Tf_df, !filter)
# Bug in tm_map(txt_corpus, removePunctuation, preserve_intra_word_dashes=TRUE) ???
# "net-a-porter" gets converted to "net-aporter"
#grep("net-a-porter", txt_vctr, ignore.case=TRUE, value=TRUE)
#grep("maser-laser", txt_vctr, ignore.case=TRUE, value=TRUE)
#txt_corpus[[which(grepl("net-a-porter", txt_vctr, ignore.case=TRUE))]]
#grep("\\b(across|longer)-(\\w)", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
#grep("(\\w)-(affected|term)\\b", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
print(sprintf("nrow(cmpnd_Tf_df): %d", nrow(cmpnd_Tf_df)))
myprint_df(cmpnd_Tf_df)
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text_reporting_compound_terms"), major.inc=FALSE)
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining compound terms in %s: ", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
# find_cmpnd_wrds(txt_vctr)
#grep("thirty-five", txt_vctr, ignore.case=TRUE, value=TRUE)
#rex_str <- glb_txt_map_df[grepl("hirty", glb_txt_map_df$rex_str), "rex_str"]
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "build.corpus"), major.inc=TRUE)
get_DTM_terms <- function(DTM) {
TfIdf_mtrx <- as.matrix(DTM)
rownames(TfIdf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
TfIdf_vctr <- colSums(TfIdf_mtrx)
names(TfIdf_vctr) <- dimnames(DTM)[[2]]
TfIdf_df <- as.data.frame(TfIdf_vctr)
names(TfIdf_df) <- "TfIdf"
TfIdf_df$term <- rownames(TfIdf_df)
TfIdf_df$freq <- colSums(TfIdf_mtrx != 0)
TfIdf_df$pos <- 1:nrow(TfIdf_df)
return(TfIdf_df <- orderBy(~ -TfIdf, TfIdf_df))
}
get_corpus_terms <- function(txt_corpus) {
TfIdf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTfIdf))
return(TfIdf_df <- get_DTM_terms(TfIdf_DTM))
}
#stop(here")
glb_corpus_lst <- list()
print(sprintf("Building glb_corpus_lst..."))
glb_corpus_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_corpus <- Corpus(VectorSource(glb_txt_lst[[txt_var]]))
#tolower Not needed as of version 0.6.2 ?
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy=FALSE)
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy=FALSE) #nuppr
# removePunctuation does not replace with whitespace. Use a custom transformer ???
txt_corpus <- tm_map(txt_corpus, removePunctuation, lazy=TRUE) #npnct<chr_ix>
# txt-corpus <- tm_map(txt_corpus, content_transformer(function(x, pattern) gsub(pattern, "", x))
txt_corpus <- tm_map(txt_corpus, removeWords,
c(glb_append_stop_words[[txt_var]],
stopwords("english")), lazy=TRUE) #nstopwrds
#print("StoppedWords:"); stopped_words_TfIdf_df <- inspect_terms(txt_corpus)
#stopped_words_TfIdf_df[grepl("cond", stopped_words_TfIdf_df$term, ignore.case=TRUE), ]
#txt_X_mtrx <- as.matrix(DocumentTermMatrix(txt_corpus, control=list(weighting=weightTfIdf)))
#which(txt_X_mtrx[, 211] > 0)
#glb_allobs_df[which(txt_X_mtrx[, 211] > 0), glb_txt_vars]
#txt_X_mtrx[2159, txt_X_mtrx[2159, ] > 0]
# txt_corpus <- tm_map(txt_corpus, stemDocument, "english", lazy=TRUE) #Done below
#txt_corpus <- tm_map(txt_corpus, content_transformer(stemDocument))
#print("StemmedWords:"); stemmed_words_TfIdf_df <- inspect_terms(txt_corpus)
#stemmed_words_TfIdf_df[grepl("cond", stemmed_words_TfIdf_df$term, ignore.case=TRUE), ]
#stm_X_mtrx <- as.matrix(DocumentTermMatrix(txt_corpus, control=list(weighting=weightTfIdf)))
#glb_allobs_df[which((stm_X_mtrx[, 180] > 0) | (stm_X_mtrx[, 181] > 0)), glb_txt_vars]
#glb_allobs_df[which((stm_X_mtrx[, 181] > 0)), glb_txt_vars]
# glb_corpus_lst[[txt_var]] <- txt_corpus
}
names(glb_corpus_lst) <- glb_txt_vars
#stop(here")
glb_post_stop_words_terms_df_lst <- list();
glb_post_stop_words_TfIdf_mtrx_lst <- list();
glb_post_stem_words_terms_df_lst <- list();
glb_post_stem_words_TfIdf_mtrx_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf(" Top_n stop TfIDf terms for %s:", txt_var))
# This impacts stemming probably due to lazy parameter
print(myprint_df(full_TfIdf_df <- get_corpus_terms(glb_corpus_lst[[txt_var]]),
glb_top_n[[txt_var]]))
glb_post_stop_words_terms_df_lst[[txt_var]] <- full_TfIdf_df
TfIdf_stop_mtrx <- as.matrix(DocumentTermMatrix(glb_corpus_lst[[txt_var]],
control=list(weighting=weightTfIdf)))
rownames(TfIdf_stop_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
glb_post_stop_words_TfIdf_mtrx_lst[[txt_var]] <- TfIdf_stop_mtrx
tmp_allobs_df <- glb_allobs_df[, c(glb_id_var, glb_rsp_var)]
tmp_allobs_df$terms.n.post.stop <- rowSums(TfIdf_stop_mtrx > 0)
tmp_allobs_df$terms.n.post.stop.log <- log(1 + tmp_allobs_df$terms.n.post.stop)
tmp_allobs_df$TfIdf.sum.post.stop <- rowSums(TfIdf_stop_mtrx)
print(sprintf(" Top_n stem TfIDf terms for %s:", txt_var))
glb_corpus_lst[[txt_var]] <- tm_map(glb_corpus_lst[[txt_var]], stemDocument,
"english", lazy=TRUE) #Features ???
print(myprint_df(full_TfIdf_df <- get_corpus_terms(glb_corpus_lst[[txt_var]]),
glb_top_n[[txt_var]]))
glb_post_stem_words_terms_df_lst[[txt_var]] <- full_TfIdf_df
TfIdf_stem_mtrx <- as.matrix(DocumentTermMatrix(glb_corpus_lst[[txt_var]],
control=list(weighting=weightTfIdf)))
rownames(TfIdf_stem_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
glb_post_stem_words_TfIdf_mtrx_lst[[txt_var]] <- TfIdf_stem_mtrx
tmp_allobs_df$terms.n.post.stem <- rowSums(TfIdf_stem_mtrx > 0)
tmp_allobs_df$terms.n.post.stem.log <- log(1 + tmp_allobs_df$terms.n.post.stem)
tmp_allobs_df$TfIdf.sum.post.stem <- rowSums(TfIdf_stem_mtrx)
tmp_allobs_df$terms.n.stem.stop.Ratio <-
1.0 * tmp_allobs_df$terms.n.post.stem / tmp_allobs_df$terms.n.post.stop
tmp_allobs_df[is.nan(tmp_allobs_df$terms.n.stem.stop.Ratio),
"terms.n.stem.stop.Ratio"] <- 1.0
tmp_allobs_df$TfIdf.sum.stem.stop.Ratio <-
1.0 * tmp_allobs_df$TfIdf.sum.post.stem / tmp_allobs_df$TfIdf.sum.post.stop
tmp_allobs_df[is.nan(tmp_allobs_df$TfIdf.sum.stem.stop.Ratio),
"TfIdf.sum.stem.stop.Ratio"] <- 1.0
tmp_trnobs_df <- tmp_allobs_df[!is.na(tmp_allobs_df[, glb_rsp_var]), ]
print(cor(as.matrix(tmp_trnobs_df[, -c(1, 2)]),
as.numeric(tmp_trnobs_df[, glb_rsp_var])))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
tmp_allobs_df <- tmp_allobs_df[, -c(1, 2)]
names(tmp_allobs_df) <- paste(paste0(txt_var_pfx, "."), names(tmp_allobs_df),
sep="")
glb_allobs_df <- cbind(glb_allobs_df, tmp_allobs_df)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
paste(txt_var_pfx, c("terms.n.post.stop", "terms.n.post.stem")))
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "extract.DTM"), major.inc=TRUE)
#stop(here")
glb_full_DTM_lst <- list(); glb_sprs_DTM_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf("Extracting TfIDf terms for %s...", txt_var))
txt_corpus <- glb_corpus_lst[[txt_var]]
# full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
# control=list(weighting=weightTf))
full_TfIdf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTfIdf))
sprs_TfIdf_DTM <- removeSparseTerms(full_TfIdf_DTM,
glb_sprs_thresholds[txt_var])
# glb_full_DTM_lst[[txt_var]] <- full_Tf_DTM
# glb_sprs_DTM_lst[[txt_var]] <- sprs_Tf_DTM
glb_full_DTM_lst[[txt_var]] <- full_TfIdf_DTM
glb_sprs_DTM_lst[[txt_var]] <- sprs_TfIdf_DTM
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "report.DTM"), major.inc=TRUE)
require(reshape2)
for (txt_var in glb_txt_vars) {
print(sprintf("Reporting TfIDf terms for %s...", txt_var))
full_TfIdf_DTM <- glb_full_DTM_lst[[txt_var]]
sprs_TfIdf_DTM <- glb_sprs_DTM_lst[[txt_var]]
print(" Full TermMatrix:"); print(full_TfIdf_DTM)
full_TfIdf_df <- get_DTM_terms(full_TfIdf_DTM)
full_TfIdf_df <- full_TfIdf_df[, c(2, 1, 3, 4)]
col_names <- names(full_TfIdf_df)
col_names[2:length(col_names)] <-
paste(col_names[2:length(col_names)], ".full", sep="")
names(full_TfIdf_df) <- col_names
# full_TfIdf_mtrx <- as.matrix(full_TfIdf_DTM)
# rownames(full_TfIdf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
# full_TfIdf_vctr <- colSums(full_TfIdf_mtrx)
# names(full_TfIdf_vctr) <- dimnames(full_TfIdf_DTM)[[2]]
# full_TfIdf_df <- as.data.frame(full_TfIdf_vctr)
# names(full_TfIdf_df) <- "TfIdf.full"
# full_TfIdf_df$term <- rownames(full_TfIdf_df)
# full_TfIdf_df$freq.full <- colSums(full_TfIdf_mtrx != 0)
# full_TfIdf_df <- orderBy(~ -TfIdf.full, full_TfIdf_df)
print(" Sparse TermMatrix:"); print(sprs_TfIdf_DTM)
sprs_TfIdf_df <- get_DTM_terms(sprs_TfIdf_DTM)
sprs_TfIdf_df <- sprs_TfIdf_df[, c(2, 1, 3, 4)]
col_names <- names(sprs_TfIdf_df)
col_names[2:length(col_names)] <-
paste(col_names[2:length(col_names)], ".sprs", sep="")
names(sprs_TfIdf_df) <- col_names
# sprs_TfIdf_vctr <- colSums(as.matrix(sprs_TfIdf_DTM))
# names(sprs_TfIdf_vctr) <- dimnames(sprs_TfIdf_DTM)[[2]]
# sprs_TfIdf_df <- as.data.frame(sprs_TfIdf_vctr)
# names(sprs_TfIdf_df) <- "TfIdf.sprs"
# sprs_TfIdf_df$term <- rownames(sprs_TfIdf_df)
# sprs_TfIdf_df$freq.sprs <- colSums(as.matrix(sprs_TfIdf_DTM) != 0)
# sprs_TfIdf_df <- orderBy(~ -TfIdf.sprs, sprs_TfIdf_df)
terms_TfIdf_df <- merge(full_TfIdf_df, sprs_TfIdf_df, all.x=TRUE)
terms_TfIdf_df$in.sprs <- !is.na(terms_TfIdf_df$freq.sprs)
plt_TfIdf_df <- subset(terms_TfIdf_df,
TfIdf.full >= min(terms_TfIdf_df$TfIdf.sprs, na.rm=TRUE))
plt_TfIdf_df$label <- ""
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "label"] <-
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"]
# glb_important_terms[[txt_var]] <- union(glb_important_terms[[txt_var]],
# plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"])
print(myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
colorcol_name="in.sprs") +
geom_text(aes(label=label), color="Black", size=3.5))
melt_TfIdf_df <- orderBy(~ -value, melt(terms_TfIdf_df, id.var="term"))
print(ggplot(melt_TfIdf_df, aes(value, color=variable)) + stat_ecdf() +
geom_hline(yintercept=glb_sprs_thresholds[txt_var],
linetype = "dotted"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, !is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(melt_TfIdf_df, "term", "value",
colorcol_name="variable"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(head(melt_TfIdf_df, 10), "term", "value",
colorcol_name="variable"))
}
# sav_full_DTM_lst <- glb_full_DTM_lst
# sav_sprs_DTM_lst <- glb_sprs_DTM_lst
# print(identical(sav_glb_corpus_lst, glb_corpus_lst))
# print(all.equal(length(sav_glb_corpus_lst), length(glb_corpus_lst)))
# print(all.equal(names(sav_glb_corpus_lst), names(glb_corpus_lst)))
# print(all.equal(sav_glb_corpus_lst[["Headline"]], glb_corpus_lst[["Headline"]]))
# print(identical(sav_full_DTM_lst, glb_full_DTM_lst))
# print(identical(sav_sprs_DTM_lst, glb_sprs_DTM_lst))
rm(full_TfIdf_mtrx, full_TfIdf_df, melt_TfIdf_df, terms_TfIdf_df)
# Create txt features
if ((length(glb_txt_vars) > 1) &&
(length(unique(pfxs <- sapply(glb_txt_vars,
function(txt) toupper(substr(txt, 1, 1))))) < length(glb_txt_vars)))
stop("Prefixes for corpus freq terms not unique: ", pfxs)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DTM"),
major.inc=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (txt_var in glb_txt_vars) {
print(sprintf("Binding DTM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_full_X_df <- as.data.frame(as.matrix(glb_full_DTM_lst[[txt_var]]))
terms_full_df <- get_DTM_terms(glb_full_DTM_lst[[txt_var]])
colnames(txt_full_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_full_X_df)), sep="")
rownames(txt_full_X_df) <- rownames(glb_allobs_df) # warning otherwise
if (glb_filter_txt_terms == "sparse") {
txt_X_df <- as.data.frame(as.matrix(glb_sprs_DTM_lst[[txt_var]]))
colnames(txt_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_X_df)), sep="")
rownames(txt_X_df) <- rownames(glb_allobs_df) # warning otherwise
} else if (glb_filter_txt_terms == "top") {
txt_X_df <- txt_full_X_df[, terms_full_df$pos[1:glb_top_n[[txt_var]]], FALSE]
} else stop("glb_filter_txt_terms should be one of c('sparse', 'top') vs. '",
glb_filter_txt_terms, "'")
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df) # TfIdf is normalized
#glb_allobs_df <- cbind(glb_allobs_df, log_X_df) # if using non-normalized metrics
}
#identical(chk_entity_df, glb_allobs_df)
#chk_entity_df <- glb_allobs_df
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DXM"),
major.inc=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
glb_punct_vctr <- c("!", "\"", "#", "\\$", "%", "&", "'",
"\\(|\\)",# "\\(", "\\)",
"\\*", "\\+", ",", "-", "\\.", "/", ":", ";",
"<|>", # "<",
"=",
# ">",
"\\?", "@", "\\[", "\\\\", "\\]", "^", "_", "`",
"\\{", "\\|", "\\}", "~")
txt_X_df <- glb_allobs_df[, c(glb_id_var, ".rnorm"), FALSE]
txt_X_df <- foreach(txt_var=glb_txt_vars, .combine=cbind) %dopar% {
#for (txt_var in glb_txt_vars) {
print(sprintf("Binding DXM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_full_DTM_mtrx <- as.matrix(glb_full_DTM_lst[[txt_var]])
rownames(txt_full_DTM_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
#print(txt_full_DTM_mtrx[txt_full_DTM_mtrx[, "ebola"] != 0, "ebola"])
# Create <txt_var>.T.<term> for glb_important_terms
for (term in glb_important_terms[[txt_var]])
txt_X_df[, paste0(txt_var_pfx, ".T.", make.names(term))] <-
txt_full_DTM_mtrx[, term]
# Create <txt_var>.nwrds.log & .nwrds.unq.log
txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")] <-
log(1 + mycount_pattern_occ("\\w+", glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".nwrds.unq.log")] <-
log(1 + rowSums(txt_full_DTM_mtrx != 0))
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] <-
rowSums(txt_full_DTM_mtrx)
txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <-
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] /
(exp(txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")]) - 1)
txt_X_df[is.nan(txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")]),
paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <- 0
# Create <txt_var>.nchrs.log
txt_X_df[, paste0(txt_var_pfx, ".nchrs.log")] <-
log(1 + mycount_pattern_occ(".", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".nuppr.log")] <-
log(1 + mycount_pattern_occ("[[:upper:]]", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".ndgts.log")] <-
log(1 + mycount_pattern_occ("[[:digit:]]", glb_allobs_df[, txt_var]))
# Create <txt_var>.npnct?.log
# would this be faster if it's iterated over each row instead of
# each created column ???
for (punct_ix in 1:length(glb_punct_vctr)) {
# smp0 <- " "
# smp1 <- "! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ _ ` { | } ~"
# smp2 <- paste(smp1, smp1, sep=" ")
# print(sprintf("Testing %s pattern:", glb_punct_vctr[punct_ix]))
# results <- mycount_pattern_occ(glb_punct_vctr[punct_ix], c(smp0, smp1, smp2))
# names(results) <- NULL; print(results)
txt_X_df[,
paste0(txt_var_pfx, ".npnct", sprintf("%02d", punct_ix), ".log")] <-
log(1 + mycount_pattern_occ(glb_punct_vctr[punct_ix],
glb_allobs_df[, txt_var]))
}
# print(head(glb_allobs_df[glb_allobs_df[, "A.npnct23.log"] > 0,
# c("UniqueID", "Popular", "Abstract", "A.npnct23.log")]))
# Create <txt_var>.nstopwrds.log & <txt_var>ratio.nstopwrds.nwrds
stop_words_rex_str <- paste0("\\b(", paste0(c(glb_append_stop_words[[txt_var]],
stopwords("english")), collapse="|"),
")\\b")
txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] <-
log(1 + mycount_pattern_occ(stop_words_rex_str, glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".ratio.nstopwrds.nwrds")] <-
exp(txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] -
txt_X_df[, paste0(txt_var_pfx, ".nwrds", ".log")])
# Create <txt_var>.P.http
txt_X_df[, paste(txt_var_pfx, ".P.http", sep="")] <-
as.integer(0 + mycount_pattern_occ("http", glb_allobs_df[, txt_var]))
# Create <txt_var>.P.mini & air
txt_X_df[, paste(txt_var_pfx, ".P.mini", sep="")] <-
as.integer(0 + mycount_pattern_occ("mini(?!m)", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.air", sep="")] <-
as.integer(0 + mycount_pattern_occ("(?<![fhp])air", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df <- subset(txt_X_df, select=-.rnorm)
txt_X_df <- txt_X_df[, -grep(glb_id_var, names(txt_X_df), fixed=TRUE), FALSE]
#glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
}
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
#myplot_box(glb_allobs_df, "A.sum.TfIdf", glb_rsp_var)
# if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
# stop("Why is this happening ?")
# Generate summaries
# print(summary(glb_allobs_df))
# print(sapply(names(glb_allobs_df), function(col) sum(is.na(glb_allobs_df[, col]))))
# print(summary(glb_trnobs_df))
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(summary(glb_newobs_df))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_txt_vars)
rm(log_X_df, txt_X_df)
}## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: stringr
## Loading required package: tm
## Loading required package: NLP
##
## Attaching package: 'NLP'
##
## The following object is masked from 'package:ggplot2':
##
## annotate## label step_major step_minor bgn end
## 2 extract.features_factorize.str.vars 2 0 14.734 15.983
## 3 extract.features_process.text 3 0 15.983 NA
## elapsed
## 2 1.249
## 3 NA
## [1] "Building glb_txt_lst..."
## [1] "running gsub for 10 (of 178): #\\bCentral African Republic\\b#..."
## [1] "running gsub for 20 (of 178): #\\bAlejandro G\\. Iñárritu#..."
## [1] "running gsub for 30 (of 178): #\\bC\\.A\\.A\\.#..."
## [1] "running gsub for 40 (of 178): #\\bCV\\.#..."
## [1] "running gsub for 50 (of 178): #\\bE\\.P\\.A\\.#..."
## [1] "running gsub for 60 (of 178): #\\bG\\.I\\. Joe#..."
## [1] "running gsub for 70 (of 178): #\\bISIS\\.#..."
## [1] "running gsub for 80 (of 178): #\\bJ\\.K\\. Simmons#..."
## [1] "running gsub for 90 (of 178): #\\bM\\. Henri Pol#..."
## [1] "running gsub for 100 (of 178): #\\bN\\.Y\\.S\\.E\\.#..."
## [1] "running gsub for 110 (of 178): #\\bR\\.B\\.S\\.#..."
## [1] "running gsub for 120 (of 178): #\\bSteven A\\. Cohen#..."
## [1] "running gsub for 130 (of 178): #\\bV\\.A\\.#..."
## [1] "running gsub for 140 (of 178): #\\bWall Street#..."
## [1] "running gsub for 150 (of 178): #\\bSaint( |-)((Laurent|Lucia)\\b)+#..."
## [1] "running gsub for 160 (of 178): #\\bSouth( |\\\\.)(America|American|Africa|African|Carolina|Dakota|Korea|Korean|Sudan)\\b#..."
## [1] "running gsub for 170 (of 178): #(\\w)-a-year#..."
## [1] "Remaining OK in descr.my:"## Loading required package: sqldf
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk## pattern .n
## 1 OK 6
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN DEVICE: Problem with Apple ID"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN SCREEN"
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## [1] "Remaining Acronyms in descr.my:"
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## pattern .n
## 1 CONDITION. 8
## 2 ONLY. 6
## 3 GB. 4
## 4 BOX. 2
## 5 CORNER. 2
## 6 ESN. 2
## 7 GOOD. 2
## 8 ICLOUD. 2
## 9 IPADS. 2
## 10 LOCKED. 2
## 11 LOCKS. 2
## 12 ONLY. 2
## 13 SCRATCHES. 2
## 14 TEARS. 2
## 15 USE. 2
## [1] "Remaining #\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+# terms in descr.my: "
## pattern .n
## 2 New Open 3
## 4 New Condition 2
## 7 New Digitizer 1
## 8 New Opened 1
## 9 New Scratch 1
## 10 New Screen 1
## [1] " consider cleaning if relevant to problem domain; geography name; .n > 1"
## [1] "Remaining #\\b(N|S|E|W|C)( |\\.)(\\w)+# terms in descr.my: "
## pattern .n
## 1 C Stock 3
## 2 W blue 1
## [1] "Remaining #\\b(North|South|East|West|Central)( |\\.)(\\w)+# terms in descr.my: "
## label step_major
## 3 extract.features_process.text 3
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor bgn end elapsed
## 3 0 15.983 18.02 2.037
## 4 1 18.021 NA NA
## [1] "Remaining compound terms in descr.my: "
## label step_major
## 4 extract.features_process.text_reporting_compound_terms 3
## 5 extract.features_build.corpus 4
## step_minor bgn end elapsed
## 4 1 18.021 18.025 0.004
## 5 0 18.026 NA NA
## [1] "Building glb_corpus_lst..."
## [1] " Top_n stop TfIDf terms for descr.my:"## Warning in weighting(x): empty document(s): character(0) character(0)
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## character(0) charact## [1] "Rows: 729; Cols: 4"
## TfIdf term freq pos
## condition 208.1205 condition 498 149
## new 125.5866 new 156 433
## used 123.4473 used 240 695
## good 120.9670 good 197 289
## scratches 114.0567 scratches 254 570
## screen 106.6170 screen 210 572
## TfIdf term freq pos
## scratch 30.068378 scratch 25 568
## days 6.562908 days 4 184
## taken 5.878172 taken 6 649
## outer 5.557938 outer 5 458
## lot 3.602633 lot 2 386
## greeting 2.075117 greeting 2 294
## TfIdf term freq pos
## 975 1.1375583 975 1 16
## blemish 1.1375583 blemish 1 83
## cables 1.1375583 cables 1 106
## engravement 1.1375583 engravement 1 226
## handling 1.1375583 handling 1 304
## 79in 0.9479652 79in 1 15
## TfIdf term freq pos
## 975 1.1375583 975 1 16
## blemish 1.1375583 blemish 1 83
## cables 1.1375583 cables 1 106
## engravement 1.1375583 engravement 1 226
## handling 1.1375583 handling 1 304
## 79in 0.9479652 79in 1 15## Warning in weighting(x): empty document(s): character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [1] " Top_n stem TfIDf terms for descr.my:"## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [1] "Rows: 588; Cols: 4"
## TfIdf term freq pos
## condit 208.1066 condit 496 122
## use 146.5910 use 291 559
## scratch 128.3886 scratch 286 457
## new 125.5866 new 156 346
## good 121.0564 good 197 233
## ipad 107.4871 ipad 232 275
## TfIdf term freq pos
## set 17.367419 set 14 469
## purpos 3.372064 purpos 2 418
## first 2.939748 first 2 206
## spent 2.635069 spent 2 503
## oem 2.275117 oem 1 355
## refund 1.421948 refund 1 434
## TfIdf term freq pos
## remot 1.2639536 remot 1 437
## ringer 1.2639536 ringer 1 450
## septemb 1.2639536 septemb 1 468
## site 1.2639536 site 1 487
## 975 1.1375583 975 1 16
## 79in 0.9479652 79in 1 15
## TfIdf term freq pos
## remot 1.2639536 remot 1 437
## ringer 1.2639536 ringer 1 450
## septemb 1.2639536 septemb 1 468
## site 1.2639536 site 1 487
## 975 1.1375583 975 1 16
## 79in 0.9479652 79in 1 15## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [,1]
## terms.n.post.stop -0.08493199
## terms.n.post.stop.log -0.10435993
## TfIdf.sum.post.stop -0.12289243
## terms.n.post.stem -0.08417074
## terms.n.post.stem.log -0.10404513
## TfIdf.sum.post.stem -0.12021745
## terms.n.stem.stop.Ratio 0.04445385
## TfIdf.sum.stem.stop.Ratio 0.09957967
## label step_major step_minor bgn end elapsed
## 5 extract.features_build.corpus 4 0 18.026 29.22 11.194
## 6 extract.features_extract.DTM 5 0 29.221 NA NA
## [1] "Extracting TfIDf terms for descr.my..."## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## label step_major step_minor bgn end elapsed
## 6 extract.features_extract.DTM 5 0 29.221 30.543 1.322
## 7 extract.features_report.DTM 6 0 30.544 NA NA## Loading required package: reshape2## [1] "Reporting TfIDf terms for descr.my..."
## [1] " Full TermMatrix:"
## <<DocumentTermMatrix (documents: 2657, terms: 588)>>
## Non-/sparse entries: 8269/1554047
## Sparsity : 99%
## Maximal term length: 16
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)
## [1] " Sparse TermMatrix:"
## <<DocumentTermMatrix (documents: 2657, terms: 8)>>
## Non-/sparse entries: 2069/19187
## Sparsity : 90%
## Maximal term length: 7
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)## Warning in myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
## colorcol_name = "in.sprs"): converting in.sprs to class:factor
## Warning: Removed 6 rows containing missing values (geom_path).## Warning: Removed 6 rows containing missing values (geom_path).## Warning: Removed 6 rows containing missing values (geom_path).
## Warning in rm(full_TfIdf_mtrx, full_TfIdf_df, melt_TfIdf_df,
## terms_TfIdf_df): object 'full_TfIdf_mtrx' not found## label step_major step_minor bgn end elapsed
## 7 extract.features_report.DTM 6 0 30.544 33.789 3.245
## 8 extract.features_bind.DTM 7 0 33.790 NA NA
## [1] "Binding DTM for descr.my..."
## label step_major step_minor bgn end elapsed
## 8 extract.features_bind.DTM 7 0 33.790 34.218 0.429
## 9 extract.features_bind.DXM 8 0 34.219 NA NA
## [1] "Binding DXM for descr.my..."## Warning in rm(log_X_df, txt_X_df): object 'log_X_df' not found
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
# dsp_obs(list(description.contains="mini(?!m)"), perl=TRUE, cols="D.P.mini", all=TRUE)
# dsp_obs(list(D.P.mini=1), cols="D.P.mini", all=TRUE)
# dsp_obs(list(D.P.mini=1, productline="Unknown"), cols="D.P.mini", all=TRUE)
# dsp_obs(list(description.contains="(?<![fhp])air"), perl=TRUE, all=TRUE)
# dsp_obs(list(description.contains="air"), perl=FALSE, cols="D.P.air", all=TRUE)
# dsp_obs(list(D.P.air=1, productline="Unknown"), cols="D.P.air", all=TRUE)
glb_allobs_df[(glb_allobs_df$D.P.mini == 1) & (glb_allobs_df$productline == "Unknown"), "prdline.my"] <- "iPad mini"
print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.mini", glb_rsp_var)))## prdline.my productline D.P.mini startprice .n
## 1 iPad 2 iPad 2 0 0.99 38
## 2 iPad mini iPad mini 0 0.99 30
## 3 Unknown Unknown 0 0.99 26
## 4 iPad 1 iPad 1 0 0.99 26
## 5 iPad 1 iPad 1 0 50.00 22
## 6 iPad mini iPad mini 0 150.00 20
## 7 iPad Air iPad Air 0 0.99 17
## 8 iPad 2 iPad 2 0 150.00 16
## 9 iPad 4 iPad 4 0 0.99 15
## 10 iPad mini iPad mini 0 100.00 14
## 11 iPad 2 iPad 2 0 100.00 13
## 12 iPad Air 2 iPad Air 2 0 0.99 13
## 13 iPad mini 2 iPad mini 2 0 0.99 13
## 14 iPad 1 iPad 1 0 80.00 12
## 15 iPad 3 iPad 3 0 0.99 12
## 16 iPad 3 iPad 3 0 200.00 12
## 17 iPad 1 iPad 1 0 90.00 11
## 18 iPad 2 iPad 2 0 175.00 11
## 19 Unknown Unknown 0 150.00 10
## 20 iPad 1 iPad 1 0 75.00 10
## 21 iPad 1 iPad 1 0 100.00 10
## 22 iPad 2 iPad 2 0 0.01 10
## 23 iPad 3 iPad 3 0 250.00 10
## 24 iPad mini iPad mini 0 50.00 10
## 25 iPad mini iPad mini 0 99.99 10
## 26 Unknown Unknown 0 100.00 9
## 27 iPad 2 iPad 2 0 99.99 9
## 28 iPad 2 iPad 2 0 149.99 9
## 29 iPad 2 iPad 2 0 199.99 9
## 30 iPad Air iPad Air 0 300.00 9
## 31 iPad mini iPad mini 0 199.99 9
## 32 Unknown Unknown 0 300.00 8
## 33 iPad 1 iPad 1 0 95.00 8
## 34 iPad 2 iPad 2 0 99.00 8
## 35 iPad 2 iPad 2 0 125.00 8
## 36 iPad 2 iPad 2 0 200.00 8
## 37 iPad 4 iPad 4 0 249.99 8
## 38 iPad Air 2 iPad Air 2 0 550.00 8
## 39 iPad mini iPad mini 0 200.00 8
## 40 iPad mini 2 iPad mini 2 0 350.00 8
## 41 Unknown Unknown 0 50.00 7
## 42 iPad 1 iPad 1 0 70.00 7
## 43 iPad 2 iPad 2 0 9.99 7
## 44 iPad 2 iPad 2 0 75.00 7
## 45 iPad 2 iPad 2 0 180.00 7
## 46 iPad 4 iPad 4 0 199.99 7
## 47 iPad mini iPad mini 0 99.00 7
## 48 iPad mini 3 iPad mini 3 0 0.99 7
## 49 iPad 1 iPad 1 0 1.00 6
## 50 iPad 2 iPad 2 0 50.00 6
## 51 iPad 2 iPad 2 0 160.00 6
## 52 iPad 4 iPad 4 0 100.00 6
## 53 iPad 4 iPad 4 0 150.00 6
## 54 iPad 4 iPad 4 0 279.99 6
## 55 iPad Air iPad Air 0 1.00 6
## 56 iPad Air iPad Air 0 200.00 6
## 57 iPad Air iPad Air 0 400.00 6
## 58 iPad Air 2 iPad Air 2 0 450.00 6
## 59 iPad mini iPad mini 0 75.00 6
## 60 iPad mini iPad mini 0 89.99 6
## 61 iPad mini iPad mini 0 159.99 6
## 62 iPad mini iPad mini 0 175.00 6
## 63 iPad 1 iPad 1 0 29.99 5
## 64 iPad 1 iPad 1 0 55.00 5
## 65 iPad 1 iPad 1 0 79.99 5
## 66 iPad 1 iPad 1 0 99.00 5
## 67 iPad 2 iPad 2 0 80.00 5
## 68 iPad 2 iPad 2 0 165.00 5
## 69 iPad 2 iPad 2 0 179.00 5
## 70 iPad 3 iPad 3 0 99.00 5
## 71 iPad 3 iPad 3 0 150.00 5
## 72 iPad 3 iPad 3 0 220.00 5
## 73 iPad 3 iPad 3 0 225.00 5
## 74 iPad 3 iPad 3 0 300.00 5
## 75 iPad 4 iPad 4 0 250.00 5
## 76 iPad 4 iPad 4 0 400.00 5
## 77 iPad Air iPad Air 0 100.00 5
## 78 iPad Air iPad Air 0 250.00 5
## 79 iPad Air iPad Air 0 350.00 5
## 80 iPad Air iPad Air 0 389.99 5
## 81 iPad Air 2 iPad Air 2 0 499.99 5
## 82 iPad mini iPad mini 0 1.00 5
## 83 iPad mini iPad mini 0 250.00 5
## 84 iPad mini iPad mini 0 350.00 5
## 85 iPad mini 2 iPad mini 2 0 200.00 5
## 86 iPad mini 2 iPad mini 2 0 225.00 5
## 87 Unknown Unknown 0 25.00 4
## 88 Unknown Unknown 0 149.99 4
## 89 Unknown Unknown 0 250.00 4
## 90 iPad 1 iPad 1 0 40.00 4
## 91 iPad 1 iPad 1 0 49.99 4
## 92 iPad 1 iPad 1 0 79.00 4
## 93 iPad 1 iPad 1 0 105.00 4
## 94 iPad 1 iPad 1 0 110.00 4
## 95 iPad 2 iPad 2 0 1.00 4
## 96 iPad 2 iPad 2 0 40.00 4
## 97 iPad 2 iPad 2 0 49.99 4
## 98 iPad 2 iPad 2 0 130.00 4
## 99 iPad 2 iPad 2 0 140.00 4
## 100 iPad 2 iPad 2 0 155.00 4
## 101 iPad 2 iPad 2 0 164.99 4
## 102 iPad 2 iPad 2 0 174.99 4
## 103 iPad 2 iPad 2 0 179.99 4
## 104 iPad 2 iPad 2 0 189.99 4
## 105 iPad 2 iPad 2 0 250.00 4
## 106 iPad 3 iPad 3 0 9.99 4
## 107 iPad 3 iPad 3 0 100.00 4
## 108 iPad 3 iPad 3 0 149.99 4
## 109 iPad 3 iPad 3 0 175.00 4
## 110 iPad 3 iPad 3 0 199.99 4
## 111 iPad 3 iPad 3 0 219.99 4
## 112 iPad 3 iPad 3 0 249.99 4
## 113 iPad 3 iPad 3 0 275.00 4
## 114 iPad 4 iPad 4 0 0.01 4
## 115 iPad 4 iPad 4 0 99.99 4
## 116 iPad 4 iPad 4 0 200.00 4
## 117 iPad 4 iPad 4 0 299.00 4
## 118 iPad Air iPad Air 0 199.99 4
## 119 iPad Air iPad Air 0 229.00 4
## 120 iPad Air iPad Air 0 279.99 4
## 121 iPad Air iPad Air 0 325.00 4
## 122 iPad Air iPad Air 0 329.99 4
## 123 iPad Air iPad Air 0 500.00 4
## 124 iPad Air 2 iPad Air 2 0 250.00 4
## 125 iPad Air 2 iPad Air 2 0 350.00 4
## 126 iPad Air 2 iPad Air 2 0 399.00 4
## 127 iPad Air 2 iPad Air 2 0 399.99 4
## 128 iPad Air 2 iPad Air 2 0 400.00 4
## 129 iPad Air 2 iPad Air 2 0 499.00 4
## 130 iPad Air 2 iPad Air 2 0 500.00 4
## 131 iPad Air 2 iPad Air 2 0 549.99 4
## 132 iPad mini iPad mini 0 119.99 4
## 133 iPad mini iPad mini 0 130.00 4
## 134 iPad mini iPad mini 0 199.00 4
## 135 iPad mini iPad mini 0 275.00 4
## 136 iPad mini iPad mini 0 300.00 4
## 137 iPad mini iPad mini 1 0.99 4
## 138 iPad mini 2 iPad mini 2 0 175.00 4
## 139 iPad mini 2 iPad mini 2 0 250.00 4
## 140 iPad mini 3 iPad mini 3 0 325.00 4
## 141 iPad mini 3 iPad mini 3 0 499.99 4
## 142 iPad mini 3 iPad mini 3 0 599.99 4
## 143 Unknown Unknown 0 15.00 3
## 144 Unknown Unknown 0 40.00 3
## 145 Unknown Unknown 0 75.00 3
## 146 Unknown Unknown 0 99.00 3
## 147 Unknown Unknown 0 120.00 3
## 148 Unknown Unknown 0 199.00 3
## 149 Unknown Unknown 0 199.99 3
## 150 Unknown Unknown 0 200.00 3
## 151 Unknown Unknown 0 249.00 3
## 152 Unknown Unknown 0 249.99 3
## 153 Unknown Unknown 0 299.99 3
## 154 Unknown Unknown 0 319.00 3
## 155 Unknown Unknown 0 350.00 3
## 156 iPad 1 iPad 1 0 0.01 3
## 157 iPad 1 iPad 1 0 19.99 3
## 158 iPad 1 iPad 1 0 20.00 3
## 159 iPad 1 iPad 1 0 25.00 3
## 160 iPad 1 iPad 1 0 30.00 3
## 161 iPad 1 iPad 1 0 36.95 3
## 162 iPad 1 iPad 1 0 65.00 3
## 163 iPad 1 iPad 1 0 84.99 3
## 164 iPad 1 iPad 1 0 85.00 3
## 165 iPad 1 iPad 1 0 89.00 3
## 166 iPad 1 iPad 1 0 99.99 3
## 167 iPad 1 iPad 1 0 119.99 3
## 168 iPad 1 iPad 1 0 150.00 3
## 169 iPad 1 iPad 1 0 180.00 3
## 170 iPad 2 iPad 2 0 30.00 3
## 171 iPad 2 iPad 2 0 70.00 3
## 172 iPad 2 iPad 2 0 85.00 3
## 173 iPad 2 iPad 2 0 89.99 3
## 174 iPad 2 iPad 2 0 90.00 3
## 175 iPad 2 iPad 2 0 120.00 3
## 176 iPad 2 iPad 2 0 129.95 3
## 177 iPad 2 iPad 2 0 129.99 3
## 178 iPad 2 iPad 2 0 139.00 3
## 179 iPad 2 iPad 2 0 149.00 3
## 180 iPad 2 iPad 2 0 149.95 3
## 181 iPad 2 iPad 2 0 154.00 3
## 182 iPad 2 iPad 2 0 159.99 3
## 183 iPad 2 iPad 2 0 169.00 3
## 184 iPad 2 iPad 2 0 249.97 3
## 185 iPad 2 iPad 2 0 275.00 3
## 186 iPad 2 iPad 2 0 300.00 3
## 187 iPad 3 iPad 3 0 1.00 3
## 188 iPad 3 iPad 3 0 10.00 3
## 189 iPad 3 iPad 3 0 99.99 3
## 190 iPad 3 iPad 3 0 128.00 3
## 191 iPad 3 iPad 3 0 185.00 3
## 192 iPad 3 iPad 3 0 187.50 3
## 193 iPad 3 iPad 3 0 199.00 3
## 194 iPad 4 iPad 4 0 50.00 3
## 195 iPad 4 iPad 4 0 225.00 3
## 196 iPad 4 iPad 4 0 259.99 3
## 197 iPad 4 iPad 4 0 275.00 3
## 198 iPad 4 iPad 4 0 280.00 3
## 199 iPad 4 iPad 4 0 300.00 3
## 200 iPad 4 iPad 4 0 320.00 3
## 201 iPad Air iPad Air 0 90.00 3
## 202 iPad Air iPad Air 0 290.00 3
## 203 iPad Air iPad Air 0 299.99 3
## 204 iPad Air iPad Air 0 320.00 3
## 205 iPad Air iPad Air 0 349.00 3
## 206 iPad Air iPad Air 0 379.00 3
## 207 iPad Air iPad Air 0 415.00 3
## 208 iPad Air iPad Air 0 449.99 3
## 209 iPad Air 2 iPad Air 2 0 1.00 3
## 210 iPad Air 2 iPad Air 2 0 50.00 3
## 211 iPad Air 2 iPad Air 2 0 199.99 3
## 212 iPad Air 2 iPad Air 2 0 425.00 3
## 213 iPad Air 2 iPad Air 2 0 439.99 3
## 214 iPad Air 2 iPad Air 2 0 480.00 3
## 215 iPad Air 2 iPad Air 2 0 525.00 3
## 216 iPad Air 2 iPad Air 2 0 560.00 3
## 217 iPad mini iPad mini 0 0.01 3
## 218 iPad mini iPad mini 0 20.00 3
## 219 iPad mini iPad mini 0 25.00 3
## 220 iPad mini iPad mini 0 45.00 3
## 221 iPad mini iPad mini 0 60.00 3
## 222 iPad mini iPad mini 0 125.00 3
## 223 iPad mini iPad mini 0 149.00 3
## 224 iPad mini iPad mini 0 160.00 3
## 225 iPad mini iPad mini 0 179.99 3
## 226 iPad mini iPad mini 0 189.99 3
## 227 iPad mini iPad mini 0 210.00 3
## 228 iPad mini iPad mini 0 249.99 3
## 229 iPad mini iPad mini 0 259.99 3
## 230 iPad mini iPad mini 0 290.00 3
## 231 iPad mini iPad mini 0 400.00 3
## 232 iPad mini 2 iPad mini 2 0 100.00 3
## 233 iPad mini 2 iPad mini 2 0 120.00 3
## 234 iPad mini 2 iPad mini 2 0 180.00 3
## 235 iPad mini 2 iPad mini 2 0 285.00 3
## 236 iPad mini 2 iPad mini 2 0 300.00 3
## 237 iPad mini 2 iPad mini 2 0 375.00 3
## 238 iPad mini 3 iPad mini 3 0 99.00 3
## 239 iPad mini 3 iPad mini 3 0 300.00 3
## 240 iPad mini 3 iPad mini 3 0 329.99 3
## 241 iPad mini 3 iPad mini 3 0 350.00 3
## 242 iPad mini 3 iPad mini 3 0 399.99 3
## 243 iPad mini 3 iPad mini 3 0 400.00 3
## 244 iPad mini 3 iPad mini 3 0 449.99 3
## 245 iPad mini 3 iPad mini 3 0 729.99 3
## 246 Unknown Unknown 0 5.00 2
## 247 Unknown Unknown 0 9.99 2
## 248 Unknown Unknown 0 19.99 2
## 249 Unknown Unknown 0 20.00 2
## 250 Unknown Unknown 0 39.99 2
## 251 Unknown Unknown 0 70.00 2
## 252 Unknown Unknown 0 79.95 2
## 253 Unknown Unknown 0 80.00 2
## 254 Unknown Unknown 0 99.99 2
## 255 Unknown Unknown 0 108.00 2
## 256 Unknown Unknown 0 159.99 2
## 257 Unknown Unknown 0 165.00 2
## 258 Unknown Unknown 0 169.99 2
## 259 Unknown Unknown 0 175.00 2
## 260 Unknown Unknown 0 185.00 2
## 261 Unknown Unknown 0 280.00 2
## 262 Unknown Unknown 0 319.99 2
## 263 Unknown Unknown 0 375.00 2
## 264 Unknown Unknown 0 399.00 2
## 265 Unknown Unknown 0 450.00 2
## 266 Unknown Unknown 0 500.00 2
## 267 Unknown Unknown 0 550.00 2
## 268 Unknown Unknown 0 599.99 2
## 269 Unknown Unknown 0 700.00 2
## 270 iPad 1 iPad 1 0 9.50 2
## 271 iPad 1 iPad 1 0 9.99 2
## 272 iPad 1 iPad 1 0 10.00 2
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## 275 iPad 1 iPad 1 0 45.00 2
## 276 iPad 1 iPad 1 0 58.00 2
## 277 iPad 1 iPad 1 0 60.00 2
## 278 iPad 1 iPad 1 0 62.00 2
## 279 iPad 1 iPad 1 0 69.00 2
## 280 iPad 1 iPad 1 0 69.99 2
## 281 iPad 1 iPad 1 0 89.95 2
## 282 iPad 1 iPad 1 0 92.14 2
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## 290 iPad 1 iPad 1 0 175.00 2
## 291 iPad 1 iPad 1 0 250.00 2
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## 293 iPad 2 iPad 2 0 0.10 2
## 294 iPad 2 iPad 2 0 15.00 2
## 295 iPad 2 iPad 2 0 19.95 2
## 296 iPad 2 iPad 2 0 59.99 2
## 297 iPad 2 iPad 2 0 65.00 2
## 298 iPad 2 iPad 2 0 69.99 2
## 299 iPad 2 iPad 2 0 74.99 2
## 300 iPad 2 iPad 2 0 89.00 2
## 301 iPad 2 iPad 2 0 95.00 2
## 302 iPad 2 iPad 2 0 119.99 2
## 303 iPad 2 iPad 2 0 128.00 2
## 304 iPad 2 iPad 2 0 135.00 2
## 305 iPad 2 iPad 2 0 144.99 2
## 306 iPad 2 iPad 2 0 145.00 2
## 307 iPad 2 iPad 2 0 149.97 2
## 308 iPad 2 iPad 2 0 150.99 2
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## 311 iPad 2 iPad 2 0 170.00 2
## 312 iPad 2 iPad 2 0 172.00 2
## 313 iPad 2 iPad 2 0 179.95 2
## 314 iPad 2 iPad 2 0 204.00 2
## 315 iPad 2 iPad 2 0 220.00 2
## 316 iPad 2 iPad 2 0 350.00 2
## 317 iPad 3 iPad 3 0 0.01 2
## 318 iPad 3 iPad 3 0 25.00 2
## 319 iPad 3 iPad 3 0 49.99 2
## 320 iPad 3 iPad 3 0 89.99 2
## 321 iPad 3 iPad 3 0 99.95 2
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## 325 iPad 3 iPad 3 0 180.00 2
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## 327 iPad 3 iPad 3 0 215.00 2
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## 329 iPad 3 iPad 3 0 239.88 2
## 330 iPad 3 iPad 3 0 239.99 2
## 331 iPad 3 iPad 3 0 299.00 2
## 332 iPad 3 iPad 3 0 314.99 2
## 333 iPad 3 iPad 3 0 450.00 2
## 334 iPad 4 iPad 4 0 80.00 2
## 335 iPad 4 iPad 4 0 99.98 2
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## 337 iPad 4 iPad 4 0 125.00 2
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## 339 iPad 4 iPad 4 0 199.00 2
## 340 iPad 4 iPad 4 0 209.00 2
## 341 iPad 4 iPad 4 0 240.00 2
## 342 iPad 4 iPad 4 0 255.00 2
## 343 iPad 4 iPad 4 0 265.00 2
## 344 iPad 4 iPad 4 0 269.99 2
## 345 iPad 4 iPad 4 0 285.00 2
## 346 iPad 4 iPad 4 0 295.00 2
## 347 iPad 4 iPad 4 0 299.99 2
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## 349 iPad 4 iPad 4 0 309.99 2
## 350 iPad 4 iPad 4 0 310.00 2
## 351 iPad 4 iPad 4 0 315.00 2
## 352 iPad 4 iPad 4 0 324.99 2
## 353 iPad 4 iPad 4 0 325.00 2
## 354 iPad 4 iPad 4 0 344.00 2
## 355 iPad 4 iPad 4 0 350.00 2
## 356 iPad 4 iPad 4 0 367.97 2
## 357 iPad 4 iPad 4 0 375.00 2
## 358 iPad 4 iPad 4 0 500.00 2
## 359 iPad 4 iPad 4 0 588.18 2
## 360 iPad Air iPad Air 0 49.99 2
## 361 iPad Air iPad Air 0 75.00 2
## 362 iPad Air iPad Air 0 89.99 2
## 363 iPad Air iPad Air 0 99.99 2
## 364 iPad Air iPad Air 0 149.99 2
## 365 iPad Air iPad Air 0 199.00 2
## 366 iPad Air iPad Air 0 209.00 2
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## 368 iPad Air iPad Air 0 249.98 2
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## 371 iPad Air iPad Air 0 280.00 2
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## 389 iPad Air 2 iPad Air 2 0 200.00 2
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## 403 iPad Air 2 iPad Air 2 0 650.00 2
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## 405 iPad Air 2 iPad Air 2 0 749.00 2
## 406 iPad Air 2 iPad Air 2 0 749.95 2
## 407 iPad Air 2 iPad Air 2 0 800.00 2
## 408 iPad mini Unknown 1 149.99 2
## 409 iPad mini iPad mini 0 5.00 2
## 410 iPad mini iPad mini 0 10.00 2
## 411 iPad mini iPad mini 0 30.00 2
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## 430 iPad mini iPad mini 0 285.00 2
## 431 iPad mini iPad mini 0 299.99 2
## 432 iPad mini iPad mini 1 179.00 2
## 433 iPad mini iPad mini 1 199.00 2
## 434 iPad mini 2 iPad mini 2 0 1.00 2
## 435 iPad mini 2 iPad mini 2 0 99.00 2
## 436 iPad mini 2 iPad mini 2 0 99.99 2
## 437 iPad mini 2 iPad mini 2 0 187.99 2
## 438 iPad mini 2 iPad mini 2 0 230.00 2
## 439 iPad mini 2 iPad mini 2 0 235.00 2
## 440 iPad mini 2 iPad mini 2 0 269.00 2
## 441 iPad mini 2 iPad mini 2 0 275.00 2
## 442 iPad mini 2 iPad mini 2 0 280.00 2
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## 444 iPad mini 2 iPad mini 2 0 299.00 2
## 445 iPad mini 2 iPad mini 2 0 315.00 2
## 446 iPad mini 2 iPad mini 2 0 325.00 2
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## 448 iPad mini 2 iPad mini 2 0 329.99 2
## 449 iPad mini 2 iPad mini 2 0 349.99 2
## 450 iPad mini 2 iPad mini 2 0 399.99 2
## 451 iPad mini 2 iPad mini 2 0 499.00 2
## 452 iPad mini 3 iPad mini 3 0 0.01 2
## 453 iPad mini 3 iPad mini 3 0 199.00 2
## 454 iPad mini 3 iPad mini 3 0 284.99 2
## 455 iPad mini 3 iPad mini 3 0 299.99 2
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## 457 iPad mini 3 iPad mini 3 0 349.00 2
## 458 iPad mini 3 iPad mini 3 0 375.00 2
## 459 iPad mini 3 iPad mini 3 0 389.99 2
## 460 iPad mini 3 iPad mini 3 0 498.88 2
## 461 iPad mini 3 iPad mini 3 0 500.00 2
## 462 Unknown Unknown 0 0.01 1
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## 465 Unknown Unknown 0 3.99 1
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## 593 Unknown Unknown 0 679.99 1
## 594 iPad 1 iPad 1 0 9.95 1
## 595 iPad 1 iPad 1 0 19.95 1
## 596 iPad 1 iPad 1 0 29.95 1
## 597 iPad 1 iPad 1 0 33.00 1
## 598 iPad 1 iPad 1 0 35.00 1
## 599 iPad 1 iPad 1 0 39.99 1
## 600 iPad 1 iPad 1 0 42.00 1
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## 602 iPad 1 iPad 1 0 48.99 1
## 603 iPad 1 iPad 1 0 49.49 1
## 604 iPad 1 iPad 1 0 52.99 1
## 605 iPad 1 iPad 1 0 54.99 1
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## 608 iPad 1 iPad 1 0 59.00 1
## 609 iPad 1 iPad 1 0 59.99 1
## 610 iPad 1 iPad 1 0 64.99 1
## 611 iPad 1 iPad 1 0 72.00 1
## 612 iPad 1 iPad 1 0 74.00 1
## 613 iPad 1 iPad 1 0 74.50 1
## 614 iPad 1 iPad 1 0 74.99 1
## 615 iPad 1 iPad 1 0 78.00 1
## 616 iPad 1 iPad 1 0 79.94 1
## 617 iPad 1 iPad 1 0 82.95 1
## 618 iPad 1 iPad 1 0 82.98 1
## 619 iPad 1 iPad 1 0 85.95 1
## 620 iPad 1 iPad 1 0 89.50 1
## 621 iPad 1 iPad 1 0 91.00 1
## 622 iPad 1 iPad 1 0 92.00 1
## 623 iPad 1 iPad 1 0 93.00 1
## 624 iPad 1 iPad 1 0 94.99 1
## 625 iPad 1 iPad 1 0 96.00 1
## 626 iPad 1 iPad 1 0 98.00 1
## 627 iPad 1 iPad 1 0 99.94 1
## 628 iPad 1 iPad 1 0 102.00 1
## 629 iPad 1 iPad 1 0 104.70 1
## 630 iPad 1 iPad 1 0 109.00 1
## 631 iPad 1 iPad 1 0 109.98 1
## 632 iPad 1 iPad 1 0 112.99 1
## 633 iPad 1 iPad 1 0 114.94 1
## 634 iPad 1 iPad 1 0 119.00 1
## 635 iPad 1 iPad 1 0 120.00 1
## 636 iPad 1 iPad 1 0 120.02 1
## 637 iPad 1 iPad 1 0 124.99 1
## 638 iPad 1 iPad 1 0 129.95 1
## 639 iPad 1 iPad 1 0 130.00 1
## 640 iPad 1 iPad 1 0 145.00 1
## 641 iPad 1 iPad 1 0 149.00 1
## 642 iPad 1 iPad 1 0 149.95 1
## 643 iPad 1 iPad 1 0 149.98 1
## 644 iPad 1 iPad 1 0 149.99 1
## 645 iPad 1 iPad 1 0 155.00 1
## 646 iPad 1 iPad 1 0 159.95 1
## 647 iPad 1 iPad 1 0 169.95 1
## 648 iPad 1 iPad 1 0 170.00 1
## 649 iPad 1 iPad 1 0 174.99 1
## 650 iPad 1 iPad 1 0 190.45 1
## 651 iPad 1 iPad 1 0 198.00 1
## 652 iPad 1 iPad 1 0 199.99 1
## 653 iPad 1 iPad 1 0 200.00 1
## 654 iPad 1 iPad 1 0 209.90 1
## 655 iPad 1 iPad 1 0 220.00 1
## 656 iPad 1 iPad 1 0 225.00 1
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## 658 iPad 1 iPad 1 0 229.00 1
## 659 iPad 1 iPad 1 0 229.97 1
## 660 iPad 1 iPad 1 0 229.99 1
## 661 iPad 1 iPad 1 0 235.00 1
## 662 iPad 1 iPad 1 0 245.00 1
## 663 iPad 1 iPad 1 0 269.99 1
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## 665 iPad 1 iPad 1 0 289.95 1
## 666 iPad 1 iPad 1 0 499.00 1
## 667 iPad 2 iPad 2 0 5.00 1
## 668 iPad 2 iPad 2 0 20.00 1
## 669 iPad 2 iPad 2 0 25.00 1
## 670 iPad 2 iPad 2 0 29.95 1
## 671 iPad 2 iPad 2 0 29.99 1
## 672 iPad 2 iPad 2 0 39.99 1
## 673 iPad 2 iPad 2 0 45.00 1
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## 675 iPad 2 iPad 2 0 60.00 1
## 676 iPad 2 iPad 2 0 66.99 1
## 677 iPad 2 iPad 2 0 69.50 1
## 678 iPad 2 iPad 2 0 69.95 1
## 679 iPad 2 iPad 2 0 71.99 1
## 680 iPad 2 iPad 2 0 72.00 1
## 681 iPad 2 iPad 2 0 74.00 1
## 682 iPad 2 iPad 2 0 79.00 1
## 683 iPad 2 iPad 2 0 79.97 1
## 684 iPad 2 iPad 2 0 89.95 1
## 685 iPad 2 iPad 2 0 92.00 1
## 686 iPad 2 iPad 2 0 92.49 1
## 687 iPad 2 iPad 2 0 96.00 1
## 688 iPad 2 iPad 2 0 97.50 1
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## 690 iPad 2 iPad 2 0 106.95 1
## 691 iPad 2 iPad 2 0 109.99 1
## 692 iPad 2 iPad 2 0 110.00 1
## 693 iPad 2 iPad 2 0 111.00 1
## 694 iPad 2 iPad 2 0 111.50 1
## 695 iPad 2 iPad 2 0 112.00 1
## 696 iPad 2 iPad 2 0 115.00 1
## 697 iPad 2 iPad 2 0 119.95 1
## 698 iPad 2 iPad 2 0 121.00 1
## 699 iPad 2 iPad 2 0 124.00 1
## 700 iPad 2 iPad 2 0 127.99 1
## 701 iPad 2 iPad 2 0 134.34 1
## 702 iPad 2 iPad 2 0 134.95 1
## 703 iPad 2 iPad 2 0 139.50 1
## 704 iPad 2 iPad 2 0 139.98 1
## 705 iPad 2 iPad 2 0 141.09 1
## 706 iPad 2 iPad 2 0 142.25 1
## 707 iPad 2 iPad 2 0 142.49 1
## 708 iPad 2 iPad 2 0 145.49 1
## 709 iPad 2 iPad 2 0 146.99 1
## 710 iPad 2 iPad 2 0 147.59 1
## 711 iPad 2 iPad 2 0 147.72 1
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## 713 iPad 2 iPad 2 0 153.95 1
## 714 iPad 2 iPad 2 0 153.99 1
## 715 iPad 2 iPad 2 0 157.00 1
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## 717 iPad 2 iPad 2 0 159.93 1
## 718 iPad 2 iPad 2 0 159.94 1
## 719 iPad 2 iPad 2 0 159.95 1
## 720 iPad 2 iPad 2 0 164.00 1
## 721 iPad 2 iPad 2 0 169.98 1
## 722 iPad 2 iPad 2 0 171.00 1
## 723 iPad 2 iPad 2 0 173.00 1
## 724 iPad 2 iPad 2 0 182.00 1
## 725 iPad 2 iPad 2 0 184.99 1
## 726 iPad 2 iPad 2 0 189.00 1
## 727 iPad 2 iPad 2 0 189.85 1
## 728 iPad 2 iPad 2 0 189.95 1
## 729 iPad 2 iPad 2 0 190.00 1
## 730 iPad 2 iPad 2 0 190.45 1
## 731 iPad 2 iPad 2 0 193.00 1
## 732 iPad 2 iPad 2 0 194.00 1
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## 734 iPad 2 iPad 2 0 194.95 1
## 735 iPad 2 iPad 2 0 195.00 1
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## 737 iPad 2 iPad 2 0 199.00 1
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## 739 iPad 2 iPad 2 0 209.00 1
## 740 iPad 2 iPad 2 0 210.00 1
## 741 iPad 2 iPad 2 0 211.95 1
## 742 iPad 2 iPad 2 0 212.99 1
## 743 iPad 2 iPad 2 0 215.00 1
## 744 iPad 2 iPad 2 0 215.99 1
## 745 iPad 2 iPad 2 0 219.85 1
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## 748 iPad 2 iPad 2 0 228.59 1
## 749 iPad 2 iPad 2 0 229.99 1
## 750 iPad 2 iPad 2 0 234.99 1
## 751 iPad 2 iPad 2 0 235.00 1
## 752 iPad 2 iPad 2 0 239.00 1
## 753 iPad 2 iPad 2 0 239.99 1
## 754 iPad 2 iPad 2 0 249.95 1
## 755 iPad 2 iPad 2 0 255.00 1
## 756 iPad 2 iPad 2 0 269.94 1
## 757 iPad 2 iPad 2 0 279.00 1
## 758 iPad 2 iPad 2 0 279.99 1
## 759 iPad 2 iPad 2 0 280.00 1
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## 764 iPad 2 iPad 2 0 310.00 1
## 765 iPad 2 iPad 2 0 315.00 1
## 766 iPad 2 iPad 2 0 330.00 1
## 767 iPad 2 iPad 2 0 340.00 1
## 768 iPad 2 iPad 2 0 349.99 1
## 769 iPad 2 iPad 2 0 393.00 1
## 770 iPad 2 iPad 2 0 395.00 1
## 771 iPad 2 iPad 2 0 396.00 1
## 772 iPad 2 iPad 2 0 406.00 1
## 773 iPad 2 iPad 2 0 417.00 1
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## 775 iPad 2 iPad 2 0 525.00 1
## 776 iPad 2 iPad 2 0 700.00 1
## 777 iPad 3 iPad 3 0 0.50 1
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## 779 iPad 3 iPad 3 0 7.99 1
## 780 iPad 3 iPad 3 0 45.00 1
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## 784 iPad 3 iPad 3 0 75.00 1
## 785 iPad 3 iPad 3 0 80.00 1
## 786 iPad 3 iPad 3 0 85.00 1
## 787 iPad 3 iPad 3 0 95.00 1
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## 794 iPad 3 iPad 3 0 159.99 1
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## 796 iPad 3 iPad 3 0 179.00 1
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## 799 iPad 3 iPad 3 0 184.95 1
## 800 iPad 3 iPad 3 0 188.99 1
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## 805 iPad 3 iPad 3 0 200.29 1
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## 891 iPad 4 iPad 4 0 289.99 1
## 892 iPad 4 iPad 4 0 291.99 1
## 893 iPad 4 iPad 4 0 299.95 1
## 894 iPad 4 iPad 4 0 303.99 1
## 895 iPad 4 iPad 4 0 304.89 1
## 896 iPad 4 iPad 4 0 319.99 1
## 897 iPad 4 iPad 4 0 324.90 1
## 898 iPad 4 iPad 4 0 329.00 1
## 899 iPad 4 iPad 4 0 339.00 1
## 900 iPad 4 iPad 4 0 340.00 1
## 901 iPad 4 iPad 4 0 345.00 1
## 902 iPad 4 iPad 4 0 349.99 1
## 903 iPad 4 iPad 4 0 399.99 1
## 904 iPad 4 iPad 4 0 410.00 1
## 905 iPad 4 iPad 4 0 419.99 1
## 906 iPad 4 iPad 4 0 425.00 1
## 907 iPad 4 iPad 4 0 445.00 1
## 908 iPad 4 iPad 4 0 479.99 1
## 909 iPad 4 iPad 4 0 520.00 1
## 910 iPad 4 iPad 4 0 540.00 1
## 911 iPad 4 iPad 4 0 544.49 1
## 912 iPad 4 iPad 4 0 559.99 1
## 913 iPad 4 iPad 4 0 573.74 1
## 914 iPad 4 iPad 4 0 649.99 1
## 915 iPad 4 iPad 4 0 650.00 1
## 916 iPad 4 iPad 4 0 695.00 1
## 917 iPad 5 iPad 5 0 300.00 1
## 918 iPad Air iPad Air 0 20.00 1
## 919 iPad Air iPad Air 0 24.99 1
## 920 iPad Air iPad Air 0 25.00 1
## 921 iPad Air iPad Air 0 49.00 1
## 922 iPad Air iPad Air 0 50.00 1
## 923 iPad Air iPad Air 0 80.00 1
## 924 iPad Air iPad Air 0 99.00 1
## 925 iPad Air iPad Air 0 144.95 1
## 926 iPad Air iPad Air 0 149.00 1
## 927 iPad Air iPad Air 0 150.00 1
## 928 iPad Air iPad Air 0 160.00 1
## 929 iPad Air iPad Air 0 179.99 1
## 930 iPad Air iPad Air 0 184.99 1
## 931 iPad Air iPad Air 0 185.00 1
## 932 iPad Air iPad Air 0 187.00 1
## 933 iPad Air iPad Air 0 189.99 1
## 934 iPad Air iPad Air 0 225.00 1
## 935 iPad Air iPad Air 0 240.00 1
## 936 iPad Air iPad Air 0 242.00 1
## 937 iPad Air iPad Air 0 249.00 1
## 938 iPad Air iPad Air 0 249.99 1
## 939 iPad Air iPad Air 0 255.00 1
## 940 iPad Air iPad Air 0 256.24 1
## 941 iPad Air iPad Air 0 257.83 1
## 942 iPad Air iPad Air 0 259.99 1
## 943 iPad Air iPad Air 0 266.05 1
## 944 iPad Air iPad Air 0 269.85 1
## 945 iPad Air iPad Air 0 270.99 1
## 946 iPad Air iPad Air 0 274.00 1
## 947 iPad Air iPad Air 0 274.99 1
## 948 iPad Air iPad Air 0 275.00 1
## 949 iPad Air iPad Air 0 288.00 1
## 950 iPad Air iPad Air 0 289.79 1
## 951 iPad Air iPad Air 0 292.50 1
## 952 iPad Air iPad Air 0 294.99 1
## 953 iPad Air iPad Air 0 299.98 1
## 954 iPad Air iPad Air 0 310.00 1
## 955 iPad Air iPad Air 0 319.85 1
## 956 iPad Air iPad Air 0 322.99 1
## 957 iPad Air iPad Air 0 334.99 1
## 958 iPad Air iPad Air 0 339.99 1
## 959 iPad Air iPad Air 0 344.95 1
## 960 iPad Air iPad Air 0 346.00 1
## 961 iPad Air iPad Air 0 347.24 1
## 962 iPad Air iPad Air 0 349.95 1
## 963 iPad Air iPad Air 0 358.24 1
## 964 iPad Air iPad Air 0 359.99 1
## 965 iPad Air iPad Air 0 360.00 1
## 966 iPad Air iPad Air 0 360.24 1
## 967 iPad Air iPad Air 0 370.00 1
## 968 iPad Air iPad Air 0 374.95 1
## 969 iPad Air iPad Air 0 374.99 1
## 970 iPad Air iPad Air 0 375.99 1
## 971 iPad Air iPad Air 0 380.00 1
## 972 iPad Air iPad Air 0 384.99 1
## 973 iPad Air iPad Air 0 388.99 1
## 974 iPad Air iPad Air 0 389.00 1
## 975 iPad Air iPad Air 0 399.95 1
## 976 iPad Air iPad Air 0 404.99 1
## 977 iPad Air iPad Air 0 408.00 1
## 978 iPad Air iPad Air 0 420.00 1
## 979 iPad Air iPad Air 0 424.95 1
## 980 iPad Air iPad Air 0 429.99 1
## 981 iPad Air iPad Air 0 430.00 1
## 982 iPad Air iPad Air 0 438.00 1
## 983 iPad Air iPad Air 0 439.00 1
## 984 iPad Air iPad Air 0 439.99 1
## 985 iPad Air iPad Air 0 443.09 1
## 986 iPad Air iPad Air 0 455.00 1
## 987 iPad Air iPad Air 0 462.89 1
## 988 iPad Air iPad Air 0 469.99 1
## 989 iPad Air iPad Air 0 495.49 1
## 990 iPad Air iPad Air 0 509.99 1
## 991 iPad Air iPad Air 0 517.89 1
## 992 iPad Air iPad Air 0 539.95 1
## 993 iPad Air iPad Air 0 549.99 1
## 994 iPad Air iPad Air 0 550.00 1
## 995 iPad Air iPad Air 0 558.17 1
## 996 iPad Air iPad Air 0 565.95 1
## 997 iPad Air iPad Air 0 589.99 1
## 998 iPad Air iPad Air 0 599.99 1
## 999 iPad Air iPad Air 0 650.00 1
## 1000 iPad Air iPad Air 0 670.00 1
## 1001 iPad Air iPad Air 0 699.00 1
## 1002 iPad Air iPad Air 0 795.99 1
## 1003 iPad Air iPad Air 0 820.00 1
## 1004 iPad Air 2 iPad Air 2 0 0.01 1
## 1005 iPad Air 2 iPad Air 2 0 1.99 1
## 1006 iPad Air 2 iPad Air 2 0 9.00 1
## 1007 iPad Air 2 iPad Air 2 0 10.00 1
## 1008 iPad Air 2 iPad Air 2 0 59.00 1
## 1009 iPad Air 2 iPad Air 2 0 60.00 1
## 1010 iPad Air 2 iPad Air 2 0 99.95 1
## 1011 iPad Air 2 iPad Air 2 0 100.00 1
## 1012 iPad Air 2 iPad Air 2 0 139.00 1
## 1013 iPad Air 2 iPad Air 2 0 229.98 1
## 1014 iPad Air 2 iPad Air 2 0 295.00 1
## 1015 iPad Air 2 iPad Air 2 0 299.00 1
## 1016 iPad Air 2 iPad Air 2 0 299.99 1
## 1017 iPad Air 2 iPad Air 2 0 305.00 1
## 1018 iPad Air 2 iPad Air 2 0 310.00 1
## 1019 iPad Air 2 iPad Air 2 0 319.99 1
## 1020 iPad Air 2 iPad Air 2 0 320.00 1
## 1021 iPad Air 2 iPad Air 2 0 324.99 1
## 1022 iPad Air 2 iPad Air 2 0 339.00 1
## 1023 iPad Air 2 iPad Air 2 0 374.95 1
## 1024 iPad Air 2 iPad Air 2 0 375.00 1
## 1025 iPad Air 2 iPad Air 2 0 380.00 1
## 1026 iPad Air 2 iPad Air 2 0 389.99 1
## 1027 iPad Air 2 iPad Air 2 0 394.99 1
## 1028 iPad Air 2 iPad Air 2 0 395.00 1
## 1029 iPad Air 2 iPad Air 2 0 399.94 1
## 1030 iPad Air 2 iPad Air 2 0 399.95 1
## 1031 iPad Air 2 iPad Air 2 0 410.00 1
## 1032 iPad Air 2 iPad Air 2 0 424.55 1
## 1033 iPad Air 2 iPad Air 2 0 424.65 1
## 1034 iPad Air 2 iPad Air 2 0 424.99 1
## 1035 iPad Air 2 iPad Air 2 0 429.00 1
## 1036 iPad Air 2 iPad Air 2 0 429.95 1
## 1037 iPad Air 2 iPad Air 2 0 429.99 1
## 1038 iPad Air 2 iPad Air 2 0 430.00 1
## 1039 iPad Air 2 iPad Air 2 0 438.99 1
## 1040 iPad Air 2 iPad Air 2 0 439.98 1
## 1041 iPad Air 2 iPad Air 2 0 440.00 1
## 1042 iPad Air 2 iPad Air 2 0 444.99 1
## 1043 iPad Air 2 iPad Air 2 0 445.00 1
## 1044 iPad Air 2 iPad Air 2 0 454.00 1
## 1045 iPad Air 2 iPad Air 2 0 454.68 1
## 1046 iPad Air 2 iPad Air 2 0 459.00 1
## 1047 iPad Air 2 iPad Air 2 0 459.95 1
## 1048 iPad Air 2 iPad Air 2 0 459.99 1
## 1049 iPad Air 2 iPad Air 2 0 469.99 1
## 1050 iPad Air 2 iPad Air 2 0 485.00 1
## 1051 iPad Air 2 iPad Air 2 0 489.99 1
## 1052 iPad Air 2 iPad Air 2 0 490.00 1
## 1053 iPad Air 2 iPad Air 2 0 490.95 1
## 1054 iPad Air 2 iPad Air 2 0 495.99 1
## 1055 iPad Air 2 iPad Air 2 0 499.95 1
## 1056 iPad Air 2 iPad Air 2 0 509.00 1
## 1057 iPad Air 2 iPad Air 2 0 510.00 1
## 1058 iPad Air 2 iPad Air 2 0 514.95 1
## 1059 iPad Air 2 iPad Air 2 0 515.00 1
## 1060 iPad Air 2 iPad Air 2 0 520.00 1
## 1061 iPad Air 2 iPad Air 2 0 528.00 1
## 1062 iPad Air 2 iPad Air 2 0 529.00 1
## 1063 iPad Air 2 iPad Air 2 0 529.95 1
## 1064 iPad Air 2 iPad Air 2 0 529.99 1
## 1065 iPad Air 2 iPad Air 2 0 549.90 1
## 1066 iPad Air 2 iPad Air 2 0 549.95 1
## 1067 iPad Air 2 iPad Air 2 0 559.00 1
## 1068 iPad Air 2 iPad Air 2 0 579.99 1
## 1069 iPad Air 2 iPad Air 2 0 585.99 1
## 1070 iPad Air 2 iPad Air 2 0 589.00 1
## 1071 iPad Air 2 iPad Air 2 0 590.00 1
## 1072 iPad Air 2 iPad Air 2 0 595.00 1
## 1073 iPad Air 2 iPad Air 2 0 598.98 1
## 1074 iPad Air 2 iPad Air 2 0 600.00 1
## 1075 iPad Air 2 iPad Air 2 0 614.99 1
## 1076 iPad Air 2 iPad Air 2 0 615.99 1
## 1077 iPad Air 2 iPad Air 2 0 619.00 1
## 1078 iPad Air 2 iPad Air 2 0 619.99 1
## 1079 iPad Air 2 iPad Air 2 0 624.99 1
## 1080 iPad Air 2 iPad Air 2 0 625.00 1
## 1081 iPad Air 2 iPad Air 2 0 629.00 1
## 1082 iPad Air 2 iPad Air 2 0 630.00 1
## 1083 iPad Air 2 iPad Air 2 0 634.99 1
## 1084 iPad Air 2 iPad Air 2 0 645.00 1
## 1085 iPad Air 2 iPad Air 2 0 645.99 1
## 1086 iPad Air 2 iPad Air 2 0 649.95 1
## 1087 iPad Air 2 iPad Air 2 0 649.99 1
## 1088 iPad Air 2 iPad Air 2 0 659.49 1
## 1089 iPad Air 2 iPad Air 2 0 660.00 1
## 1090 iPad Air 2 iPad Air 2 0 675.00 1
## 1091 iPad Air 2 iPad Air 2 0 679.95 1
## 1092 iPad Air 2 iPad Air 2 0 679.99 1
## 1093 iPad Air 2 iPad Air 2 0 680.00 1
## 1094 iPad Air 2 iPad Air 2 0 710.00 1
## 1095 iPad Air 2 iPad Air 2 0 730.00 1
## 1096 iPad Air 2 iPad Air 2 0 740.00 1
## 1097 iPad Air 2 iPad Air 2 0 749.99 1
## 1098 iPad Air 2 iPad Air 2 0 785.00 1
## 1099 iPad Air 2 iPad Air 2 0 789.00 1
## 1100 iPad Air 2 iPad Air 2 0 789.99 1
## 1101 iPad Air 2 iPad Air 2 0 795.00 1
## 1102 iPad Air 2 iPad Air 2 0 798.00 1
## 1103 iPad Air 2 iPad Air 2 0 799.00 1
## 1104 iPad Air 2 iPad Air 2 0 829.99 1
## 1105 iPad Air 2 iPad Air 2 0 879.99 1
## 1106 iPad Air 2 iPad Air 2 0 899.99 1
## 1107 iPad Air 2 iPad Air 2 0 900.00 1
## 1108 iPad Air 2 iPad Air 2 0 939.00 1
## 1109 iPad mini Unknown 1 190.00 1
## 1110 iPad mini Unknown 1 409.99 1
## 1111 iPad mini Unknown 1 999.99 1
## 1112 iPad mini iPad mini 0 0.98 1
## 1113 iPad mini iPad mini 0 9.99 1
## 1114 iPad mini iPad mini 0 10.99 1
## 1115 iPad mini iPad mini 0 19.50 1
## 1116 iPad mini iPad mini 0 19.99 1
## 1117 iPad mini iPad mini 0 29.99 1
## 1118 iPad mini iPad mini 0 40.00 1
## 1119 iPad mini iPad mini 0 42.00 1
## 1120 iPad mini iPad mini 0 49.95 1
## 1121 iPad mini iPad mini 0 59.99 1
## 1122 iPad mini iPad mini 0 62.00 1
## 1123 iPad mini iPad mini 0 74.95 1
## 1124 iPad mini iPad mini 0 74.99 1
## 1125 iPad mini iPad mini 0 79.00 1
## 1126 iPad mini iPad mini 0 79.99 1
## 1127 iPad mini iPad mini 0 84.99 1
## 1128 iPad mini iPad mini 0 89.00 1
## 1129 iPad mini iPad mini 0 109.00 1
## 1130 iPad mini iPad mini 0 109.99 1
## 1131 iPad mini iPad mini 0 110.00 1
## 1132 iPad mini iPad mini 0 112.00 1
## 1133 iPad mini iPad mini 0 113.00 1
## 1134 iPad mini iPad mini 0 118.00 1
## 1135 iPad mini iPad mini 0 119.98 1
## 1136 iPad mini iPad mini 0 129.00 1
## 1137 iPad mini iPad mini 0 129.95 1
## 1138 iPad mini iPad mini 0 129.99 1
## 1139 iPad mini iPad mini 0 135.00 1
## 1140 iPad mini iPad mini 0 139.00 1
## 1141 iPad mini iPad mini 0 140.00 1
## 1142 iPad mini iPad mini 0 144.99 1
## 1143 iPad mini iPad mini 0 145.00 1
## 1144 iPad mini iPad mini 0 149.59 1
## 1145 iPad mini iPad mini 0 149.95 1
## 1146 iPad mini iPad mini 0 149.99 1
## 1147 iPad mini iPad mini 0 159.95 1
## 1148 iPad mini iPad mini 0 160.57 1
## 1149 iPad mini iPad mini 0 168.00 1
## 1150 iPad mini iPad mini 0 170.00 1
## 1151 iPad mini iPad mini 0 171.95 1
## 1152 iPad mini iPad mini 0 176.27 1
## 1153 iPad mini iPad mini 0 178.99 1
## 1154 iPad mini iPad mini 0 179.00 1
## 1155 iPad mini iPad mini 0 179.96 1
## 1156 iPad mini iPad mini 0 180.00 1
## 1157 iPad mini iPad mini 0 181.00 1
## 1158 iPad mini iPad mini 0 184.99 1
## 1159 iPad mini iPad mini 0 185.00 1
## 1160 iPad mini iPad mini 0 185.49 1
## 1161 iPad mini iPad mini 0 187.89 1
## 1162 iPad mini iPad mini 0 188.88 1
## 1163 iPad mini iPad mini 0 190.00 1
## 1164 iPad mini iPad mini 0 194.29 1
## 1165 iPad mini iPad mini 0 195.00 1
## 1166 iPad mini iPad mini 0 198.00 1
## 1167 iPad mini iPad mini 0 199.97 1
## 1168 iPad mini iPad mini 0 205.00 1
## 1169 iPad mini iPad mini 0 208.00 1
## 1170 iPad mini iPad mini 0 208.99 1
## 1171 iPad mini iPad mini 0 209.00 1
## 1172 iPad mini iPad mini 0 209.85 1
## 1173 iPad mini iPad mini 0 209.99 1
## 1174 iPad mini iPad mini 0 211.50 1
## 1175 iPad mini iPad mini 0 212.99 1
## 1176 iPad mini iPad mini 0 214.98 1
## 1177 iPad mini iPad mini 0 215.99 1
## 1178 iPad mini iPad mini 0 219.00 1
## 1179 iPad mini iPad mini 0 220.00 1
## 1180 iPad mini iPad mini 0 227.88 1
## 1181 iPad mini iPad mini 0 235.00 1
## 1182 iPad mini iPad mini 0 239.00 1
## 1183 iPad mini iPad mini 0 240.00 1
## 1184 iPad mini iPad mini 0 241.88 1
## 1185 iPad mini iPad mini 0 244.97 1
## 1186 iPad mini iPad mini 0 249.95 1
## 1187 iPad mini iPad mini 0 252.88 1
## 1188 iPad mini iPad mini 0 255.00 1
## 1189 iPad mini iPad mini 0 258.88 1
## 1190 iPad mini iPad mini 0 259.00 1
## 1191 iPad mini iPad mini 0 260.00 1
## 1192 iPad mini iPad mini 0 265.00 1
## 1193 iPad mini iPad mini 0 265.99 1
## 1194 iPad mini iPad mini 0 271.00 1
## 1195 iPad mini iPad mini 0 279.00 1
## 1196 iPad mini iPad mini 0 279.50 1
## 1197 iPad mini iPad mini 0 279.99 1
## 1198 iPad mini iPad mini 0 289.00 1
## 1199 iPad mini iPad mini 0 289.99 1
## 1200 iPad mini iPad mini 0 295.00 1
## 1201 iPad mini iPad mini 0 298.00 1
## 1202 iPad mini iPad mini 0 299.95 1
## 1203 iPad mini iPad mini 0 310.00 1
## 1204 iPad mini iPad mini 0 315.00 1
## 1205 iPad mini iPad mini 0 320.00 1
## 1206 iPad mini iPad mini 0 334.95 1
## 1207 iPad mini iPad mini 0 339.99 1
## 1208 iPad mini iPad mini 0 348.60 1
## 1209 iPad mini iPad mini 0 349.99 1
## 1210 iPad mini iPad mini 0 351.00 1
## 1211 iPad mini iPad mini 0 358.87 1
## 1212 iPad mini iPad mini 0 370.00 1
## 1213 iPad mini iPad mini 0 375.00 1
## 1214 iPad mini iPad mini 0 379.99 1
## 1215 iPad mini iPad mini 0 385.00 1
## 1216 iPad mini iPad mini 0 387.45 1
## 1217 iPad mini iPad mini 0 388.30 1
## 1218 iPad mini iPad mini 0 397.75 1
## 1219 iPad mini iPad mini 0 398.99 1
## 1220 iPad mini iPad mini 0 399.99 1
## 1221 iPad mini iPad mini 0 429.99 1
## 1222 iPad mini iPad mini 0 475.00 1
## 1223 iPad mini iPad mini 0 499.99 1
## 1224 iPad mini iPad mini 0 720.12 1
## 1225 iPad mini iPad mini 0 999.00 1
## 1226 iPad mini iPad mini 1 9.99 1
## 1227 iPad mini iPad mini 1 49.99 1
## 1228 iPad mini iPad mini 1 100.00 1
## 1229 iPad mini iPad mini 1 149.00 1
## 1230 iPad mini iPad mini 1 169.99 1
## 1231 iPad mini iPad mini 1 249.99 1
## 1232 iPad mini iPad mini 1 429.00 1
## 1233 iPad mini iPad mini 2 99.99 1
## 1234 iPad mini 2 iPad mini 2 0 0.01 1
## 1235 iPad mini 2 iPad mini 2 0 10.00 1
## 1236 iPad mini 2 iPad mini 2 0 25.00 1
## 1237 iPad mini 2 iPad mini 2 0 49.99 1
## 1238 iPad mini 2 iPad mini 2 0 79.95 1
## 1239 iPad mini 2 iPad mini 2 0 99.97 1
## 1240 iPad mini 2 iPad mini 2 0 119.00 1
## 1241 iPad mini 2 iPad mini 2 0 129.99 1
## 1242 iPad mini 2 iPad mini 2 0 130.00 1
## 1243 iPad mini 2 iPad mini 2 0 145.00 1
## 1244 iPad mini 2 iPad mini 2 0 149.00 1
## 1245 iPad mini 2 iPad mini 2 0 149.95 1
## 1246 iPad mini 2 iPad mini 2 0 150.00 1
## 1247 iPad mini 2 iPad mini 2 0 155.00 1
## 1248 iPad mini 2 iPad mini 2 0 160.00 1
## 1249 iPad mini 2 iPad mini 2 0 185.00 1
## 1250 iPad mini 2 iPad mini 2 0 199.00 1
## 1251 iPad mini 2 iPad mini 2 0 209.98 1
## 1252 iPad mini 2 iPad mini 2 0 210.00 1
## 1253 iPad mini 2 iPad mini 2 0 215.00 1
## 1254 iPad mini 2 iPad mini 2 0 217.00 1
## 1255 iPad mini 2 iPad mini 2 0 222.72 1
## 1256 iPad mini 2 iPad mini 2 0 223.00 1
## 1257 iPad mini 2 iPad mini 2 0 229.00 1
## 1258 iPad mini 2 iPad mini 2 0 237.00 1
## 1259 iPad mini 2 iPad mini 2 0 239.00 1
## 1260 iPad mini 2 iPad mini 2 0 239.99 1
## 1261 iPad mini 2 iPad mini 2 0 245.00 1
## 1262 iPad mini 2 iPad mini 2 0 248.18 1
## 1263 iPad mini 2 iPad mini 2 0 249.00 1
## 1264 iPad mini 2 iPad mini 2 0 259.95 1
## 1265 iPad mini 2 iPad mini 2 0 260.00 1
## 1266 iPad mini 2 iPad mini 2 0 264.99 1
## 1267 iPad mini 2 iPad mini 2 0 279.99 1
## 1268 iPad mini 2 iPad mini 2 0 289.95 1
## 1269 iPad mini 2 iPad mini 2 0 295.00 1
## 1270 iPad mini 2 iPad mini 2 0 299.99 1
## 1271 iPad mini 2 iPad mini 2 0 308.00 1
## 1272 iPad mini 2 iPad mini 2 0 310.00 1
## 1273 iPad mini 2 iPad mini 2 0 319.98 1
## 1274 iPad mini 2 iPad mini 2 0 319.99 1
## 1275 iPad mini 2 iPad mini 2 0 327.58 1
## 1276 iPad mini 2 iPad mini 2 0 339.00 1
## 1277 iPad mini 2 iPad mini 2 0 339.99 1
## 1278 iPad mini 2 iPad mini 2 0 376.00 1
## 1279 iPad mini 2 iPad mini 2 0 379.99 1
## 1280 iPad mini 2 iPad mini 2 0 380.00 1
## 1281 iPad mini 2 iPad mini 2 0 385.00 1
## 1282 iPad mini 2 iPad mini 2 0 387.00 1
## 1283 iPad mini 2 iPad mini 2 0 395.00 1
## 1284 iPad mini 2 iPad mini 2 0 400.00 1
## 1285 iPad mini 2 iPad mini 2 0 429.99 1
## 1286 iPad mini 2 iPad mini 2 0 430.00 1
## 1287 iPad mini 2 iPad mini 2 0 449.00 1
## 1288 iPad mini 2 iPad mini 2 0 450.00 1
## 1289 iPad mini 2 iPad mini 2 0 458.00 1
## 1290 iPad mini 2 iPad mini 2 0 460.00 1
## 1291 iPad mini 2 iPad mini 2 0 469.00 1
## 1292 iPad mini 2 iPad mini 2 0 500.00 1
## 1293 iPad mini 2 iPad mini 2 0 509.00 1
## 1294 iPad mini 2 iPad mini 2 0 550.00 1
## 1295 iPad mini 2 iPad mini 2 0 575.00 1
## 1296 iPad mini 2 iPad mini 2 0 595.00 1
## 1297 iPad mini 2 iPad mini 2 1 195.00 1
## 1298 iPad mini 2 iPad mini 2 1 201.99 1
## 1299 iPad mini 2 iPad mini 2 1 225.00 1
## 1300 iPad mini 2 iPad mini 2 1 238.80 1
## 1301 iPad mini 2 iPad mini 2 1 249.00 1
## 1302 iPad mini 2 iPad mini 2 1 300.00 1
## 1303 iPad mini 2 iPad mini 2 1 350.25 1
## 1304 iPad mini 3 iPad mini 3 0 0.45 1
## 1305 iPad mini 3 iPad mini 3 0 9.95 1
## 1306 iPad mini 3 iPad mini 3 0 25.00 1
## 1307 iPad mini 3 iPad mini 3 0 100.00 1
## 1308 iPad mini 3 iPad mini 3 0 149.00 1
## 1309 iPad mini 3 iPad mini 3 0 175.00 1
## 1310 iPad mini 3 iPad mini 3 0 197.97 1
## 1311 iPad mini 3 iPad mini 3 0 199.99 1
## 1312 iPad mini 3 iPad mini 3 0 249.00 1
## 1313 iPad mini 3 iPad mini 3 0 250.00 1
## 1314 iPad mini 3 iPad mini 3 0 290.00 1
## 1315 iPad mini 3 iPad mini 3 0 295.95 1
## 1316 iPad mini 3 iPad mini 3 0 299.00 1
## 1317 iPad mini 3 iPad mini 3 0 309.95 1
## 1318 iPad mini 3 iPad mini 3 0 329.00 1
## 1319 iPad mini 3 iPad mini 3 0 331.99 1
## 1320 iPad mini 3 iPad mini 3 0 332.50 1
## 1321 iPad mini 3 iPad mini 3 0 334.00 1
## 1322 iPad mini 3 iPad mini 3 0 335.00 1
## 1323 iPad mini 3 iPad mini 3 0 339.50 1
## 1324 iPad mini 3 iPad mini 3 0 339.98 1
## 1325 iPad mini 3 iPad mini 3 0 340.00 1
## 1326 iPad mini 3 iPad mini 3 0 349.95 1
## 1327 iPad mini 3 iPad mini 3 0 349.99 1
## 1328 iPad mini 3 iPad mini 3 0 359.00 1
## 1329 iPad mini 3 iPad mini 3 0 359.99 1
## 1330 iPad mini 3 iPad mini 3 0 370.00 1
## 1331 iPad mini 3 iPad mini 3 0 379.95 1
## 1332 iPad mini 3 iPad mini 3 0 379.99 1
## 1333 iPad mini 3 iPad mini 3 0 380.00 1
## 1334 iPad mini 3 iPad mini 3 0 385.00 1
## 1335 iPad mini 3 iPad mini 3 0 394.99 1
## 1336 iPad mini 3 iPad mini 3 0 399.00 1
## 1337 iPad mini 3 iPad mini 3 0 419.95 1
## 1338 iPad mini 3 iPad mini 3 0 425.00 1
## 1339 iPad mini 3 iPad mini 3 0 426.99 1
## 1340 iPad mini 3 iPad mini 3 0 439.99 1
## 1341 iPad mini 3 iPad mini 3 0 445.95 1
## 1342 iPad mini 3 iPad mini 3 0 449.95 1
## 1343 iPad mini 3 iPad mini 3 0 450.00 1
## 1344 iPad mini 3 iPad mini 3 0 459.99 1
## 1345 iPad mini 3 iPad mini 3 0 469.99 1
## 1346 iPad mini 3 iPad mini 3 0 475.00 1
## 1347 iPad mini 3 iPad mini 3 0 485.00 1
## 1348 iPad mini 3 iPad mini 3 0 510.00 1
## 1349 iPad mini 3 iPad mini 3 0 525.00 1
## 1350 iPad mini 3 iPad mini 3 0 529.99 1
## 1351 iPad mini 3 iPad mini 3 0 549.99 1
## 1352 iPad mini 3 iPad mini 3 0 550.00 1
## 1353 iPad mini 3 iPad mini 3 0 559.99 1
## 1354 iPad mini 3 iPad mini 3 0 569.00 1
## 1355 iPad mini 3 iPad mini 3 0 575.00 1
## 1356 iPad mini 3 iPad mini 3 0 579.99 1
## 1357 iPad mini 3 iPad mini 3 0 609.99 1
## 1358 iPad mini 3 iPad mini 3 0 614.99 1
## 1359 iPad mini 3 iPad mini 3 0 639.99 1
## 1360 iPad mini 3 iPad mini 3 0 650.00 1
## 1361 iPad mini 3 iPad mini 3 0 689.99 1
## 1362 iPad mini 3 iPad mini 3 0 799.99 1
## 1363 iPad mini 3 iPad mini 3 0 948.98 1
## 1364 iPad mini 3 iPad mini 3 1 400.00 1
## 1365 iPad mini 3 iPad mini 3 1 419.99 1
## 1366 iPad mini 3 iPad mini 3 1 460.00 1
## 1367 iPad mini 3 iPad mini 3 1 499.99 1
## 1368 iPad mini 3 iPad mini 3 1 599.99 1
## 1369 iPad mini Retina iPad mini Retina 0 160.00 1
## 1370 iPad mini Retina iPad mini Retina 0 235.00 1
## 1371 iPad mini Retina iPad mini Retina 0 250.00 1
## 1372 iPad mini Retina iPad mini Retina 0 299.00 1
## 1373 iPad mini Retina iPad mini Retina 0 339.00 1
## 1374 iPad mini Retina iPad mini Retina 0 350.00 1
## 1375 iPad mini Retina iPad mini Retina 0 420.00 1
## 1376 iPad mini Retina iPad mini Retina 1 303.67 1glb_allobs_df[glb_allobs_df$UniqueID == 11863, "D.P.air"] <- 0
glb_allobs_df[(glb_allobs_df$D.P.air == 1) & (glb_allobs_df$productline == "Unknown"), "prdline.my"] <- "iPad Air"
print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.air", glb_rsp_var)))## prdline.my productline D.P.air startprice .n
## 1 iPad 2 iPad 2 0 0.99 38
## 2 iPad mini iPad mini 0 0.99 34
## 3 iPad 1 iPad 1 0 0.99 26
## 4 Unknown Unknown 0 0.99 25
## 5 iPad 1 iPad 1 0 50.00 22
## 6 iPad mini iPad mini 0 150.00 20
## 7 iPad Air iPad Air 0 0.99 17
## 8 iPad 2 iPad 2 0 150.00 16
## 9 iPad 4 iPad 4 0 0.99 15
## 10 iPad mini iPad mini 0 100.00 15
## 11 iPad 2 iPad 2 0 100.00 13
## 12 iPad Air 2 iPad Air 2 0 0.99 13
## 13 iPad mini 2 iPad mini 2 0 0.99 13
## 14 iPad 1 iPad 1 0 80.00 12
## 15 iPad 3 iPad 3 0 0.99 12
## 16 iPad 3 iPad 3 0 200.00 12
## 17 iPad 1 iPad 1 0 90.00 11
## 18 iPad 2 iPad 2 0 175.00 11
## 19 iPad mini iPad mini 0 99.99 11
## 20 Unknown Unknown 0 150.00 10
## 21 iPad 1 iPad 1 0 75.00 10
## 22 iPad 1 iPad 1 0 100.00 10
## 23 iPad 2 iPad 2 0 0.01 10
## 24 iPad 3 iPad 3 0 250.00 10
## 25 iPad mini iPad mini 0 50.00 10
## 26 Unknown Unknown 0 100.00 9
## 27 iPad 2 iPad 2 0 99.99 9
## 28 iPad 2 iPad 2 0 149.99 9
## 29 iPad 2 iPad 2 0 199.99 9
## 30 iPad Air iPad Air 0 300.00 9
## 31 iPad mini iPad mini 0 199.99 9
## 32 Unknown Unknown 0 300.00 8
## 33 iPad 1 iPad 1 0 95.00 8
## 34 iPad 2 iPad 2 0 99.00 8
## 35 iPad 2 iPad 2 0 125.00 8
## 36 iPad 2 iPad 2 0 200.00 8
## 37 iPad 4 iPad 4 0 249.99 8
## 38 iPad Air 2 iPad Air 2 0 550.00 8
## 39 iPad mini iPad mini 0 200.00 8
## 40 iPad mini 2 iPad mini 2 0 350.00 8
## 41 Unknown Unknown 0 50.00 7
## 42 iPad 1 iPad 1 0 70.00 7
## 43 iPad 2 iPad 2 0 9.99 7
## 44 iPad 2 iPad 2 0 75.00 7
## 45 iPad 2 iPad 2 0 180.00 7
## 46 iPad 4 iPad 4 0 199.99 7
## 47 iPad mini iPad mini 0 99.00 7
## 48 iPad mini 3 iPad mini 3 0 0.99 7
## 49 iPad 1 iPad 1 0 1.00 6
## 50 iPad 2 iPad 2 0 50.00 6
## 51 iPad 2 iPad 2 0 160.00 6
## 52 iPad 4 iPad 4 0 100.00 6
## 53 iPad 4 iPad 4 0 150.00 6
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## 57 iPad Air 2 iPad Air 2 0 450.00 6
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## 67 iPad 1 iPad 1 0 99.00 5
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## 78 iPad 4 iPad 4 0 400.00 5
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## 89 iPad mini 3 iPad mini 3 0 599.99 5
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## 92 Unknown Unknown 0 250.00 4
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## 119 iPad 4 iPad 4 0 299.00 4
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## 121 iPad Air iPad Air 0 325.00 4
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## 128 iPad Air 2 iPad Air 2 0 400.00 4
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## 131 iPad mini iPad mini 0 119.99 4
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## 134 iPad mini iPad mini 0 249.99 4
## 135 iPad mini iPad mini 0 275.00 4
## 136 iPad mini iPad mini 0 300.00 4
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## 139 iPad mini 2 iPad mini 2 0 300.00 4
## 140 iPad mini 3 iPad mini 3 0 325.00 4
## 141 iPad mini 3 iPad mini 3 0 400.00 4
## 142 Unknown Unknown 0 15.00 3
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## 154 Unknown Unknown 0 350.00 3
## 155 iPad 1 iPad 1 0 0.01 3
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## 160 iPad 1 iPad 1 0 36.95 3
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## 163 iPad 1 iPad 1 0 85.00 3
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## 165 iPad 1 iPad 1 0 99.99 3
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## 194 iPad 4 iPad 4 0 50.00 3
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## 197 iPad 4 iPad 4 0 275.00 3
## 198 iPad 4 iPad 4 0 280.00 3
## 199 iPad 4 iPad 4 0 300.00 3
## 200 iPad 4 iPad 4 0 320.00 3
## 201 iPad Air iPad Air 0 90.00 3
## 202 iPad Air iPad Air 0 199.99 3
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## 204 iPad Air iPad Air 0 299.99 3
## 205 iPad Air iPad Air 0 320.00 3
## 206 iPad Air iPad Air 0 379.00 3
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## 208 iPad Air 2 iPad Air 2 0 1.00 3
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## 210 iPad Air 2 iPad Air 2 0 199.99 3
## 211 iPad Air 2 iPad Air 2 0 425.00 3
## 212 iPad Air 2 iPad Air 2 0 439.99 3
## 213 iPad Air 2 iPad Air 2 0 480.00 3
## 214 iPad Air 2 iPad Air 2 0 499.00 3
## 215 iPad Air 2 iPad Air 2 0 525.00 3
## 216 iPad Air 2 iPad Air 2 0 560.00 3
## 217 iPad mini iPad mini 0 0.01 3
## 218 iPad mini iPad mini 0 20.00 3
## 219 iPad mini iPad mini 0 25.00 3
## 220 iPad mini iPad mini 0 45.00 3
## 221 iPad mini iPad mini 0 60.00 3
## 222 iPad mini iPad mini 0 125.00 3
## 223 iPad mini iPad mini 0 160.00 3
## 224 iPad mini iPad mini 0 179.00 3
## 225 iPad mini iPad mini 0 179.99 3
## 226 iPad mini iPad mini 0 189.99 3
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## 228 iPad mini iPad mini 0 259.99 3
## 229 iPad mini iPad mini 0 290.00 3
## 230 iPad mini iPad mini 0 400.00 3
## 231 iPad mini 2 iPad mini 2 0 100.00 3
## 232 iPad mini 2 iPad mini 2 0 120.00 3
## 233 iPad mini 2 iPad mini 2 0 180.00 3
## 234 iPad mini 2 iPad mini 2 0 285.00 3
## 235 iPad mini 2 iPad mini 2 0 375.00 3
## 236 iPad mini 3 iPad mini 3 0 99.00 3
## 237 iPad mini 3 iPad mini 3 0 300.00 3
## 238 iPad mini 3 iPad mini 3 0 329.99 3
## 239 iPad mini 3 iPad mini 3 0 350.00 3
## 240 iPad mini 3 iPad mini 3 0 399.99 3
## 241 iPad mini 3 iPad mini 3 0 449.99 3
## 242 iPad mini 3 iPad mini 3 0 729.99 3
## 243 Unknown Unknown 0 5.00 2
## 244 Unknown Unknown 0 9.99 2
## 245 Unknown Unknown 0 19.99 2
## 246 Unknown Unknown 0 20.00 2
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## 261 Unknown Unknown 0 399.00 2
## 262 Unknown Unknown 0 450.00 2
## 263 Unknown Unknown 0 500.00 2
## 264 Unknown Unknown 0 599.99 2
## 265 Unknown Unknown 0 700.00 2
## 266 iPad 1 iPad 1 0 9.50 2
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## 268 iPad 1 iPad 1 0 10.00 2
## 269 iPad 1 iPad 1 0 14.99 2
## 270 iPad 1 iPad 1 0 15.00 2
## 271 iPad 1 iPad 1 0 45.00 2
## 272 iPad 1 iPad 1 0 58.00 2
## 273 iPad 1 iPad 1 0 60.00 2
## 274 iPad 1 iPad 1 0 62.00 2
## 275 iPad 1 iPad 1 0 69.00 2
## 276 iPad 1 iPad 1 0 69.99 2
## 277 iPad 1 iPad 1 0 89.95 2
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## 280 iPad 1 iPad 1 0 104.99 2
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## 286 iPad 1 iPad 1 0 175.00 2
## 287 iPad 1 iPad 1 0 250.00 2
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## 289 iPad 2 iPad 2 0 0.10 2
## 290 iPad 2 iPad 2 0 15.00 2
## 291 iPad 2 iPad 2 0 19.95 2
## 292 iPad 2 iPad 2 0 59.99 2
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## 297 iPad 2 iPad 2 0 95.00 2
## 298 iPad 2 iPad 2 0 119.99 2
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## 300 iPad 2 iPad 2 0 135.00 2
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## 303 iPad 2 iPad 2 0 149.97 2
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## 309 iPad 2 iPad 2 0 179.95 2
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## 335 iPad 4 iPad 4 0 199.00 2
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## 340 iPad 4 iPad 4 0 269.99 2
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## 351 iPad 4 iPad 4 0 350.00 2
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## 354 iPad 4 iPad 4 0 500.00 2
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## 403 iPad mini Unknown 0 149.99 2
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## 450 iPad mini 3 iPad mini 3 0 299.99 2
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## 609 iPad 1 iPad 1 0 74.99 1
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## 1252 iPad mini 2 iPad mini 2 0 149.95 1
## 1253 iPad mini 2 iPad mini 2 0 150.00 1
## 1254 iPad mini 2 iPad mini 2 0 155.00 1
## 1255 iPad mini 2 iPad mini 2 0 160.00 1
## 1256 iPad mini 2 iPad mini 2 0 185.00 1
## 1257 iPad mini 2 iPad mini 2 0 195.00 1
## 1258 iPad mini 2 iPad mini 2 0 199.00 1
## 1259 iPad mini 2 iPad mini 2 0 201.99 1
## 1260 iPad mini 2 iPad mini 2 0 209.98 1
## 1261 iPad mini 2 iPad mini 2 0 210.00 1
## 1262 iPad mini 2 iPad mini 2 0 215.00 1
## 1263 iPad mini 2 iPad mini 2 0 217.00 1
## 1264 iPad mini 2 iPad mini 2 0 222.72 1
## 1265 iPad mini 2 iPad mini 2 0 223.00 1
## 1266 iPad mini 2 iPad mini 2 0 229.00 1
## 1267 iPad mini 2 iPad mini 2 0 237.00 1
## 1268 iPad mini 2 iPad mini 2 0 238.80 1
## 1269 iPad mini 2 iPad mini 2 0 239.00 1
## 1270 iPad mini 2 iPad mini 2 0 239.99 1
## 1271 iPad mini 2 iPad mini 2 0 245.00 1
## 1272 iPad mini 2 iPad mini 2 0 248.18 1
## 1273 iPad mini 2 iPad mini 2 0 259.95 1
## 1274 iPad mini 2 iPad mini 2 0 260.00 1
## 1275 iPad mini 2 iPad mini 2 0 264.99 1
## 1276 iPad mini 2 iPad mini 2 0 279.99 1
## 1277 iPad mini 2 iPad mini 2 0 289.95 1
## 1278 iPad mini 2 iPad mini 2 0 295.00 1
## 1279 iPad mini 2 iPad mini 2 0 299.99 1
## 1280 iPad mini 2 iPad mini 2 0 308.00 1
## 1281 iPad mini 2 iPad mini 2 0 310.00 1
## 1282 iPad mini 2 iPad mini 2 0 319.98 1
## 1283 iPad mini 2 iPad mini 2 0 319.99 1
## 1284 iPad mini 2 iPad mini 2 0 327.58 1
## 1285 iPad mini 2 iPad mini 2 0 339.00 1
## 1286 iPad mini 2 iPad mini 2 0 339.99 1
## 1287 iPad mini 2 iPad mini 2 0 350.25 1
## 1288 iPad mini 2 iPad mini 2 0 376.00 1
## 1289 iPad mini 2 iPad mini 2 0 379.99 1
## 1290 iPad mini 2 iPad mini 2 0 380.00 1
## 1291 iPad mini 2 iPad mini 2 0 385.00 1
## 1292 iPad mini 2 iPad mini 2 0 387.00 1
## 1293 iPad mini 2 iPad mini 2 0 395.00 1
## 1294 iPad mini 2 iPad mini 2 0 400.00 1
## 1295 iPad mini 2 iPad mini 2 0 429.99 1
## 1296 iPad mini 2 iPad mini 2 0 430.00 1
## 1297 iPad mini 2 iPad mini 2 0 449.00 1
## 1298 iPad mini 2 iPad mini 2 0 450.00 1
## 1299 iPad mini 2 iPad mini 2 0 458.00 1
## 1300 iPad mini 2 iPad mini 2 0 460.00 1
## 1301 iPad mini 2 iPad mini 2 0 469.00 1
## 1302 iPad mini 2 iPad mini 2 0 500.00 1
## 1303 iPad mini 2 iPad mini 2 0 509.00 1
## 1304 iPad mini 2 iPad mini 2 0 550.00 1
## 1305 iPad mini 2 iPad mini 2 0 575.00 1
## 1306 iPad mini 2 iPad mini 2 0 595.00 1
## 1307 iPad mini 3 iPad mini 3 0 0.45 1
## 1308 iPad mini 3 iPad mini 3 0 9.95 1
## 1309 iPad mini 3 iPad mini 3 0 25.00 1
## 1310 iPad mini 3 iPad mini 3 0 100.00 1
## 1311 iPad mini 3 iPad mini 3 0 149.00 1
## 1312 iPad mini 3 iPad mini 3 0 175.00 1
## 1313 iPad mini 3 iPad mini 3 0 197.97 1
## 1314 iPad mini 3 iPad mini 3 0 199.99 1
## 1315 iPad mini 3 iPad mini 3 0 249.00 1
## 1316 iPad mini 3 iPad mini 3 0 250.00 1
## 1317 iPad mini 3 iPad mini 3 0 290.00 1
## 1318 iPad mini 3 iPad mini 3 0 295.95 1
## 1319 iPad mini 3 iPad mini 3 0 299.00 1
## 1320 iPad mini 3 iPad mini 3 0 309.95 1
## 1321 iPad mini 3 iPad mini 3 0 329.00 1
## 1322 iPad mini 3 iPad mini 3 0 331.99 1
## 1323 iPad mini 3 iPad mini 3 0 332.50 1
## 1324 iPad mini 3 iPad mini 3 0 334.00 1
## 1325 iPad mini 3 iPad mini 3 0 335.00 1
## 1326 iPad mini 3 iPad mini 3 0 339.50 1
## 1327 iPad mini 3 iPad mini 3 0 339.98 1
## 1328 iPad mini 3 iPad mini 3 0 340.00 1
## 1329 iPad mini 3 iPad mini 3 0 349.95 1
## 1330 iPad mini 3 iPad mini 3 0 349.99 1
## 1331 iPad mini 3 iPad mini 3 0 359.00 1
## 1332 iPad mini 3 iPad mini 3 0 359.99 1
## 1333 iPad mini 3 iPad mini 3 0 370.00 1
## 1334 iPad mini 3 iPad mini 3 0 379.95 1
## 1335 iPad mini 3 iPad mini 3 0 379.99 1
## 1336 iPad mini 3 iPad mini 3 0 380.00 1
## 1337 iPad mini 3 iPad mini 3 0 385.00 1
## 1338 iPad mini 3 iPad mini 3 0 394.99 1
## 1339 iPad mini 3 iPad mini 3 0 399.00 1
## 1340 iPad mini 3 iPad mini 3 0 419.95 1
## 1341 iPad mini 3 iPad mini 3 0 419.99 1
## 1342 iPad mini 3 iPad mini 3 0 425.00 1
## 1343 iPad mini 3 iPad mini 3 0 426.99 1
## 1344 iPad mini 3 iPad mini 3 0 439.99 1
## 1345 iPad mini 3 iPad mini 3 0 445.95 1
## 1346 iPad mini 3 iPad mini 3 0 449.95 1
## 1347 iPad mini 3 iPad mini 3 0 450.00 1
## 1348 iPad mini 3 iPad mini 3 0 459.99 1
## 1349 iPad mini 3 iPad mini 3 0 460.00 1
## 1350 iPad mini 3 iPad mini 3 0 469.99 1
## 1351 iPad mini 3 iPad mini 3 0 475.00 1
## 1352 iPad mini 3 iPad mini 3 0 485.00 1
## 1353 iPad mini 3 iPad mini 3 0 510.00 1
## 1354 iPad mini 3 iPad mini 3 0 525.00 1
## 1355 iPad mini 3 iPad mini 3 0 529.99 1
## 1356 iPad mini 3 iPad mini 3 0 549.99 1
## 1357 iPad mini 3 iPad mini 3 0 550.00 1
## 1358 iPad mini 3 iPad mini 3 0 559.99 1
## 1359 iPad mini 3 iPad mini 3 0 569.00 1
## 1360 iPad mini 3 iPad mini 3 0 575.00 1
## 1361 iPad mini 3 iPad mini 3 0 579.99 1
## 1362 iPad mini 3 iPad mini 3 0 609.99 1
## 1363 iPad mini 3 iPad mini 3 0 614.99 1
## 1364 iPad mini 3 iPad mini 3 0 639.99 1
## 1365 iPad mini 3 iPad mini 3 0 650.00 1
## 1366 iPad mini 3 iPad mini 3 0 689.99 1
## 1367 iPad mini 3 iPad mini 3 0 799.99 1
## 1368 iPad mini 3 iPad mini 3 0 948.98 1
## 1369 iPad mini Retina iPad mini Retina 0 160.00 1
## 1370 iPad mini Retina iPad mini Retina 0 235.00 1
## 1371 iPad mini Retina iPad mini Retina 0 250.00 1
## 1372 iPad mini Retina iPad mini Retina 0 299.00 1
## 1373 iPad mini Retina iPad mini Retina 0 303.67 1
## 1374 iPad mini Retina iPad mini Retina 0 339.00 1
## 1375 iPad mini Retina iPad mini Retina 0 350.00 1
## 1376 iPad mini Retina iPad mini Retina 0 420.00 1glb_allobs_df[glb_allobs_df$UniqueID == 12156, "prdline.my"] <- "iPad 1"
glb_allobs_df[glb_allobs_df$UniqueID == 11811, "prdline.my"] <- "iPad 2"
glb_allobs_df[glb_allobs_df$UniqueID == 11767, "prdline.my"] <- "iPad 2"
glb_allobs_df[glb_allobs_df$UniqueID == 11767, "storage"] <- "32"
# dsp_obs(list(prdline.my="Unknown"), all=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
tmp_allobs_df <- glb_allobs_df[, "prdline.my", FALSE]
names(tmp_allobs_df) <- "old.prdline.my"
glb_allobs_df$prdline.my <-
plyr::revalue(glb_allobs_df$prdline.my, c(
# "iPad 1" = "iPad",
# "iPad 2" = "iPad2+",
"iPad 3" = "iPad 3+",
"iPad 4" = "iPad 3+",
"iPad 5" = "iPad 3+",
"iPad Air" = "iPadAir",
"iPad Air 2" = "iPadAir",
"iPad mini" = "iPadmini",
"iPad mini 2" = "iPadmini 2+",
"iPad mini 3" = "iPadmini 2+",
"iPad mini Retina" = "iPadmini 2+"
))
tmp_allobs_df$prdline.my <- glb_allobs_df[, "prdline.my"]
print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my", "old.prdline.my")))## prdline.my old.prdline.my .n
## 1 iPad 2 iPad 2 442
## 2 iPadmini iPad mini 393
## 3 iPad 1 iPad 1 314
## 4 Unknown Unknown 285
## 5 iPadAir iPad Air 257
## 6 iPadAir iPad Air 2 233
## 7 iPad 3+ iPad 4 225
## 8 iPad 3+ iPad 3 208
## 9 iPadmini 2+ iPad mini 2 163
## 10 iPadmini 2+ iPad mini 3 128
## 11 iPadmini 2+ iPad mini Retina 8
## 12 iPad 3+ iPad 5 1print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my")))## prdline.my .n
## 1 iPadAir 490
## 2 iPad 2 442
## 3 iPad 3+ 434
## 4 iPadmini 393
## 5 iPad 1 314
## 6 iPadmini 2+ 299
## 7 Unknown 285glb_allobs_df$prdline.my.fctr <- as.factor(glb_allobs_df$prdline.my)
glb_allobs_df$storage.fctr <- as.factor(glb_allobs_df$storage)
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# print(myplot_scatter(glb_trnobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
glb_full_DTM_lst, glb_sprs_DTM_lst, txt_corpus, txt_vctr)## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'corpus_lst' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'full_TfIdf_vctr' not foundextract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, "extract.features_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 extract.features_bind.DXM 8 0 34.219 86.383 52.164
## 10 extract.features_end 9 0 86.383 NA NAmyplt_chunk(extract.features_chunk_df)## label step_major
## 9 extract.features_bind.DXM 8
## 5 extract.features_build.corpus 4
## 7 extract.features_report.DTM 6
## 3 extract.features_process.text 3
## 6 extract.features_extract.DTM 5
## 2 extract.features_factorize.str.vars 2
## 8 extract.features_bind.DTM 7
## 1 extract.features_bgn 1
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor bgn end elapsed duration
## 9 0 34.219 86.383 52.164 52.164
## 5 0 18.026 29.220 11.194 11.194
## 7 0 30.544 33.789 3.245 3.245
## 3 0 15.983 18.020 2.037 2.037
## 6 0 29.221 30.543 1.322 1.322
## 2 0 14.734 15.983 1.249 1.249
## 8 0 33.790 34.218 0.429 0.428
## 1 0 14.721 14.733 0.012 0.012
## 4 1 18.021 18.025 0.004 0.004
## [1] "Total Elapsed Time: 86.383 secs"
# if (glb_save_envir)
# save(glb_feats_df,
# glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
# file=paste0(glb_out_pfx, "extract_features_dsk.RData"))
# load(paste0(glb_out_pfx, "extract_features_dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all","data.new")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "cluster.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 5 extract.features 3 0 14.715 87.76 73.046
## 6 cluster.data 4 0 87.761 NA NAStep 4.0: cluster data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 6 cluster.data 4 0 87.761 88.809 1.048
## 7 manage.missing.data 4 1 88.810 NA NA# If mice crashes with error: Error in get(as.character(FUN), mode = "function", envir = envir) : object 'State' of mode 'function' was not found
# consider excluding 'State' as a feature
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# glb_trnobs_df <- na.omit(glb_trnobs_df)
# glb_newobs_df <- na.omit(glb_newobs_df)
# df[is.na(df)] <- 0
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## sold
## 798
## [1] "numeric data w/ 0s in : "
## biddable sold startprice.log
## 1444 999 31
## cellular.fctr D.terms.n.post.stop D.terms.n.post.stop.log
## 1597 1521 1521
## D.TfIdf.sum.post.stop D.terms.n.post.stem D.terms.n.post.stem.log
## 1521 1521 1521
## D.TfIdf.sum.post.stem D.T.condit D.T.use
## 1521 2161 2366
## D.T.scratch D.T.new D.T.good
## 2371 2501 2460
## D.T.ipad D.T.screen D.T.great
## 2425 2444 2532
## D.T.work D.T.excel D.nwrds.log
## 2459 2557 1520
## D.nwrds.unq.log D.sum.TfIdf D.ratio.sum.TfIdf.nwrds
## 1521 1521 1521
## D.nchrs.log D.nuppr.log D.ndgts.log
## 1520 1522 2426
## D.npnct01.log D.npnct02.log D.npnct03.log
## 2579 2657 2614
## D.npnct04.log D.npnct05.log D.npnct06.log
## 2657 2592 2554
## D.npnct07.log D.npnct08.log D.npnct09.log
## 2656 2581 2641
## D.npnct10.log D.npnct11.log D.npnct12.log
## 2648 2301 2537
## D.npnct13.log D.npnct14.log D.npnct15.log
## 1932 2582 2637
## D.npnct16.log D.npnct17.log D.npnct18.log
## 2546 2657 2656
## D.npnct19.log D.npnct20.log D.npnct21.log
## 2657 2657 2657
## D.npnct22.log D.npnct23.log D.npnct24.log
## 2657 2657 1520
## D.npnct25.log D.npnct26.log D.npnct27.log
## 2657 2657 2657
## D.npnct28.log D.npnct29.log D.npnct30.log
## 2649 2657 2657
## D.nstopwrds.log D.P.http D.P.mini
## 1663 2657 2623
## D.P.air
## 2637
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid prdline.my descr.my
## 0 NA 0 1520# glb_allobs_df <- na.omit(glb_allobs_df)
# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function() {
require(mice)
set.seed(glb_mice_complete.seed)
inp_impent_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
union(glb_exclude_vars_as_features, glb_rsp_var))]
print("Summary before imputation: ")
print(summary(inp_impent_df))
out_impent_df <- complete(mice(inp_impent_df))
print(summary(out_impent_df))
ret_vars <- sapply(names(out_impent_df),
function(col) ifelse(!identical(out_impent_df[, col],
inp_impent_df[, col]),
col, ""))
ret_vars <- ret_vars[ret_vars != ""]
# complete(mice()) changes attributes of factors even though values don't change
for (col in ret_vars) {
if (inherits(out_impent_df[, col], "factor")) {
if (identical(as.numeric(out_impent_df[, col]),
as.numeric(inp_impent_df[, col])))
ret_vars <- setdiff(ret_vars, col)
}
}
return(out_impent_df[, ret_vars])
}
if (glb_impute_na_data &&
(length(myfind_numerics_missing(glb_allobs_df)) > 0) &&
(ncol(nonna_df <- glb_impute_missing_data()) > 0)) {
for (col in names(nonna_df)) {
glb_allobs_df[, paste0(col, ".nonNA")] <- nonna_df[, col]
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, col)
}
}
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## sold
## 798
## [1] "numeric data w/ 0s in : "
## biddable sold startprice.log
## 1444 999 31
## cellular.fctr D.terms.n.post.stop D.terms.n.post.stop.log
## 1597 1521 1521
## D.TfIdf.sum.post.stop D.terms.n.post.stem D.terms.n.post.stem.log
## 1521 1521 1521
## D.TfIdf.sum.post.stem D.T.condit D.T.use
## 1521 2161 2366
## D.T.scratch D.T.new D.T.good
## 2371 2501 2460
## D.T.ipad D.T.screen D.T.great
## 2425 2444 2532
## D.T.work D.T.excel D.nwrds.log
## 2459 2557 1520
## D.nwrds.unq.log D.sum.TfIdf D.ratio.sum.TfIdf.nwrds
## 1521 1521 1521
## D.nchrs.log D.nuppr.log D.ndgts.log
## 1520 1522 2426
## D.npnct01.log D.npnct02.log D.npnct03.log
## 2579 2657 2614
## D.npnct04.log D.npnct05.log D.npnct06.log
## 2657 2592 2554
## D.npnct07.log D.npnct08.log D.npnct09.log
## 2656 2581 2641
## D.npnct10.log D.npnct11.log D.npnct12.log
## 2648 2301 2537
## D.npnct13.log D.npnct14.log D.npnct15.log
## 1932 2582 2637
## D.npnct16.log D.npnct17.log D.npnct18.log
## 2546 2657 2656
## D.npnct19.log D.npnct20.log D.npnct21.log
## 2657 2657 2657
## D.npnct22.log D.npnct23.log D.npnct24.log
## 2657 2657 1520
## D.npnct25.log D.npnct26.log D.npnct27.log
## 2657 2657 2657
## D.npnct28.log D.npnct29.log D.npnct30.log
## 2649 2657 2657
## D.nstopwrds.log D.P.http D.P.mini
## 1663 2657 2623
## D.P.air
## 2637
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid prdline.my descr.my
## 0 NA 0 1520Step 4.1: manage missing data
if (glb_cluster) {
require(proxy)
#require(hash)
require(dynamicTreeCut)
require(entropy)
require(tidyr)
# glb_hash <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
# glb_hash_lst <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
print("Clustering features: ")
print(cluster_vars <- grep(paste0("[",
toupper(paste0(substr(glb_txt_vars, 1, 1), collapse="")),
"]\\.[PT]\\."),
names(glb_allobs_df), value=TRUE))
print(sprintf("glb_allobs_df Entropy: %0.4f",
allobs_ent <- entropy(table(glb_allobs_df[, glb_rsp_var]), method="ML")))
category_df <- as.data.frame(table(glb_allobs_df[, glb_category_var],
glb_allobs_df[, glb_rsp_var]))
names(category_df)[c(1, 2)] <- c(glb_category_var, glb_rsp_var)
category_df <- do.call(tidyr::spread, list(category_df, glb_rsp_var, "Freq"))
tmp.entropy <- sapply(1:nrow(category_df),
function(row) entropy(as.numeric(category_df[row, -1]), method="ML"))
tmp.knt <- sapply(1:nrow(category_df),
function(row) sum(as.numeric(category_df[row, -1])))
category_df$.entropy <- tmp.entropy; category_df$.knt <- tmp.knt
print(sprintf("glb_allobs_df$%s Entropy: %0.4f (%0.4f pct)", glb_category_var,
category_ent <- weighted.mean(category_df$.entropy, category_df$.knt),
100 * category_ent / allobs_ent))
print(category_df)
glb_allobs_df$.clusterid <- 1
#print(max(table(glb_allobs_df$myCategory.fctr) / 20))
for (grp in sort(unique(glb_allobs_df[, glb_category_var]))) {
print(sprintf("Category: %s", grp))
ctgry_allobs_df <- glb_allobs_df[glb_allobs_df[, glb_category_var] == grp, ]
dstns_dist <- dist(ctgry_allobs_df[, cluster_vars], method = "cosine")
dstns_mtrx <- as.matrix(dstns_dist)
print(sprintf("max distance(%0.4f) pair:", max(dstns_mtrx)))
row_ix <- ceiling(which.max(dstns_mtrx) / ncol(dstns_mtrx))
col_ix <- which.max(dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c(glb_id_var, glb_rsp_var, glb_category_var, glb_txt_vars, cluster_vars)])
min_dstns_mtrx <- dstns_mtrx
diag(min_dstns_mtrx) <- 1
print(sprintf("min distance(%0.4f) pair:", min(min_dstns_mtrx)))
row_ix <- ceiling(which.min(min_dstns_mtrx) / ncol(min_dstns_mtrx))
col_ix <- which.min(min_dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c(glb_id_var, glb_rsp_var, glb_category_var, glb_txt_vars, cluster_vars)])
clusters <- hclust(dstns_dist, method = "ward.D2")
#plot(clusters, labels=NULL, hang=-1)
myplclust(clusters, lab.col=unclass(ctgry_allobs_df[, glb_rsp_var]))
#clusterGroups = cutree(clusters, k=7)
clusterGroups <- cutreeDynamic(clusters, minClusterSize=20, method="tree", deepSplit=0)
# Unassigned groups are labeled 0; the largest group has label 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#print(ctgry_allobs_df[which(clusterGroups == 1), c("UniqueID", "Popular", "Headline")])
#print(ctgry_allobs_df[(clusterGroups == 1) & !is.na(ctgry_allobs_df$Popular) & (ctgry_allobs_df$Popular==1), c("UniqueID", "Popular", "Headline")])
clusterGroups[clusterGroups == 0] <- 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#summary(factor(clusterGroups))
# clusterGroups <- clusterGroups +
# 100 * # has to be > max(table(glb_allobs_df[, glb_category_var].fctr) / minClusterSize=20)
# which(levels(glb_allobs_df[, glb_category_var].fctr) == grp)
# table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
# add to glb_allobs_df - then split the data again
glb_allobs_df[glb_allobs_df[, glb_category_var]==grp,]$.clusterid <- clusterGroups
#print(unique(glb_allobs_df$.clusterid))
#print(glb_feats_df[glb_feats_df$id == ".clusterid.fctr", ])
}
cluster_df <- as.data.frame(table(glb_allobs_df[, glb_category_var],
glb_allobs_df[, ".clusterid"],
glb_allobs_df[, glb_rsp_var]))
cluster_df <- subset(cluster_df, Freq > 0)
names(cluster_df)[c(1, 2, 3)] <- c(glb_category_var, ".clusterid", glb_rsp_var)
# spread(unite(cluster_df, prdline.my.clusterid, prdline.my, .clusterid),
# sold.fctr, Freq)
cluster_df <- do.call(tidyr::unite,
list(cluster_df, paste0(glb_category_var, ".clusterid"),
grep(glb_category_var, names(cluster_df)),
grep(".clusterid", names(cluster_df))))
cluster_df <- do.call(tidyr::spread,
list(cluster_df, glb_rsp_var, "Freq"))
tmp.entropy <- sapply(1:nrow(cluster_df),
function(row) entropy(as.numeric(cluster_df[row, -1]), method="ML"))
tmp.knt <- sapply(1:nrow(cluster_df),
function(row) sum(as.numeric(cluster_df[row, -1])))
cluster_df$.entropy <- tmp.entropy; cluster_df$.knt <- tmp.knt
print(sprintf("glb_allobs_df$%s$.clusterid Entropy: %0.4f (%0.4f pct)",
glb_category_var,
cluster_ent <- weighted.mean(cluster_df$.entropy, cluster_df$.knt),
100 * cluster_ent / category_ent))
print(cluster_df)
glb_allobs_df$.clusterid.fctr <- as.factor(glb_allobs_df$.clusterid)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
".clusterid")
glb_interaction_only_features[paste0(glb_category_var, ".fctr")] <-
c(".clusterid.fctr")
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
cluster_vars)
}## Loading required package: proxy
##
## Attaching package: 'proxy'
##
## The following objects are masked from 'package:stats':
##
## as.dist, dist
##
## The following object is masked from 'package:base':
##
## as.matrix
##
## Loading required package: dynamicTreeCut
## Loading required package: entropy
## Loading required package: tidyr## [1] "Clustering features: "
## [1] "D.T.condit" "D.T.use" "D.T.scratch" "D.T.new" "D.T.good"
## [6] "D.T.ipad" "D.T.screen" "D.T.great" "D.T.work" "D.T.excel"
## [11] "D.P.http" "D.P.mini" "D.P.air"
## [1] "glb_allobs_df Entropy: 5.5618"
## [1] "glb_allobs_df$prdline.my Entropy: 4.7594 (85.5734 pct)"
## prdline.my 0.01 0.1 0.45 0.5 0.98 0.99 1 1.99 2.99 3.99 4.69 4.99 5
## 1 Unknown 1 0 0 0 0 25 1 0 1 1 1 1 2
## 2 iPad 1 3 0 0 0 0 26 6 0 0 0 0 0 0
## 3 iPad 2 10 2 0 0 0 38 4 0 0 0 0 0 1
## 4 iPad 3+ 6 0 0 1 0 27 4 0 0 0 0 0 1
## 5 iPadAir 1 0 0 0 0 31 9 1 0 0 0 0 0
## 6 iPadmini 3 0 0 0 1 34 5 0 0 0 0 0 2
## 7 iPadmini 2+ 3 0 1 0 0 20 2 0 0 0 0 0 0
## 5.65 7.99 8 8.99 9 9.5 9.95 9.99 10 10.99 14 14.49 14.99 15 17.75 19.5
## 1 1 1 0 1 0 0 1 2 1 0 1 1 1 3 1 0
## 2 0 0 0 0 0 2 1 2 2 0 0 0 2 2 0 0
## 3 0 0 0 0 0 0 0 7 0 0 0 0 0 2 0 0
## 4 0 2 1 0 0 0 0 4 4 0 0 0 0 0 0 0
## 5 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 2 2 1 0 0 0 0 0 1
## 7 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0
## 19.95 19.99 20 24.99 25 28 28.75 29.95 29.99 30 30.99 32.95 33 35 36.95
## 1 0 1 2 1 4 1 1 0 0 1 1 1 0 1 0
## 2 1 3 3 0 3 0 0 1 5 3 0 0 1 1 3
## 3 2 1 1 0 1 0 0 1 1 3 0 0 0 0 0
## 4 0 0 1 0 2 0 0 0 0 0 0 0 0 1 0
## 5 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0
## 6 0 1 3 0 3 0 0 0 1 2 0 0 0 0 0
## 7 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0
## 37.98 38.99 39 39.99 40 41 42 43.2 44.99 45 48 48.99 49 49.49 49.95
## 1 1 0 0 2 2 1 0 1 1 1 0 0 0 0 1
## 2 0 0 0 1 5 0 1 0 0 2 1 1 0 1 0
## 3 0 0 0 1 4 0 0 0 0 1 0 0 1 0 0
## 4 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 6 0 0 0 0 1 0 1 0 0 3 0 0 0 0 1
## 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 49.99 50 51.99 52.25 52.99 54.99 55 55.66 56 57.5 58 59 59.95 59.99 60
## 1 1 7 1 1 0 0 0 0 0 1 1 0 1 1 0
## 2 4 22 0 0 1 1 5 1 1 0 2 1 0 1 2
## 3 4 6 0 0 0 0 0 0 0 0 0 0 0 2 1
## 4 2 4 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 2 4 0 0 0 0 0 0 0 0 0 1 0 0 1
## 6 1 10 0 0 0 0 0 0 0 0 0 0 0 1 3
## 7 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 62 63 64.99 65 66.99 69 69.5 69.69 69.95 69.99 70 70.99 71 71.99 72 74
## 1 0 1 0 1 0 1 0 0 0 0 2 1 0 0 0 0
## 2 2 0 1 3 0 2 0 0 0 2 7 0 0 0 1 1
## 3 0 0 0 2 1 0 1 0 1 2 3 0 0 1 1 1
## 4 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 1 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 74.5 74.95 74.99 75 76 78 79 79.94 79.95 79.97 79.99 80 82.95 82.98
## 1 0 0 0 3 1 0 0 0 2 0 0 2 0 0
## 2 1 0 1 10 0 1 4 1 0 0 5 12 1 1
## 3 0 0 2 7 0 0 1 0 0 1 0 5 0 0
## 4 0 0 0 1 0 0 0 0 0 0 1 3 0 0
## 5 0 0 0 2 0 0 0 0 0 0 0 1 0 0
## 6 0 1 1 6 0 0 1 0 0 0 1 0 0 0
## 7 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 84.99 85 85.95 87 89 89.5 89.95 89.99 90 91 92 92.14 92.49 93 94.99 95
## 1 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 0
## 2 3 3 1 0 3 1 2 0 11 1 1 2 0 1 1 8
## 3 0 3 0 0 2 0 1 3 3 0 1 0 1 0 0 2
## 4 0 1 0 0 0 0 0 2 0 0 0 0 0 0 0 1
## 5 0 0 0 0 0 0 0 2 3 0 0 0 0 0 0 0
## 6 1 2 0 0 1 0 0 6 2 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 96 97.5 98 99 99.46 99.75 99.94 99.95 99.97 99.98 99.99 100 101 102 104
## 1 0 0 0 3 1 0 0 0 0 0 2 9 0 0 0
## 2 1 0 1 5 0 0 1 0 0 0 3 10 2 1 0
## 3 1 1 0 8 0 0 0 0 0 0 9 13 0 0 1
## 4 0 0 0 5 0 1 0 3 0 2 7 10 0 0 0
## 5 0 0 0 1 0 0 0 1 0 0 4 6 0 0 0
## 6 0 0 0 7 0 0 0 2 0 0 11 15 0 0 0
## 7 0 0 0 5 0 0 0 0 1 0 2 4 0 0 0
## 104.7 104.99 105 106.95 107 108 109 109.98 109.99 110 111 111.5 112
## 1 0 0 0 0 0 2 0 0 1 1 0 0 0
## 2 1 2 4 0 0 0 1 1 0 4 0 0 0
## 3 0 0 0 1 0 0 0 0 1 1 1 1 1
## 4 0 0 1 0 2 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 1 0 1 1 0 0 1
## 7 0 0 0 0 0 0 0 0 0 0 0 0 0
## 112.99 113 114.48 114.94 114.99 115 116.33 118 118.84 118.95 119 119.88
## 1 0 0 1 0 0 0 1 0 1 1 0 0
## 2 1 0 0 1 0 2 0 0 0 0 1 0
## 3 0 0 0 0 0 1 0 0 0 0 0 0
## 4 0 0 0 0 0 2 0 0 0 0 0 1
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 1 0 0 2 2 0 1 0 0 0 0
## 7 0 0 0 0 0 0 0 0 0 0 1 0
## 119.95 119.98 119.99 120 120.02 121 124 124.95 124.99 125 127.95 127.99
## 1 0 0 1 3 0 0 0 0 1 1 1 0
## 2 0 0 3 1 1 0 0 2 1 2 0 0
## 3 1 0 2 3 0 1 1 0 0 8 0 1
## 4 0 0 1 1 0 0 0 0 0 4 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 1 4 0 0 0 0 0 0 3 0 0
## 7 0 0 0 3 0 0 0 0 0 0 0 0
## 128 129 129.95 129.99 130 134.34 134.61 134.95 135 137.95 139 139.5
## 1 0 0 0 0 0 0 0 0 0 1 1 0
## 2 0 0 1 2 1 0 0 0 0 0 0 0
## 3 2 0 3 3 4 1 0 1 2 0 3 1
## 4 3 1 0 0 0 0 1 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 1 0
## 6 0 1 1 1 4 0 0 0 1 0 1 0
## 7 0 0 0 1 1 0 0 0 0 0 0 0
## 139.98 139.99 140 141.09 142.25 142.49 144.5 144.95 144.99 145 145.49
## 1 0 1 0 0 0 0 0 0 0 1 0
## 2 0 0 0 0 0 0 0 0 0 1 0
## 3 1 0 4 1 1 1 0 0 2 2 1
## 4 0 1 2 0 0 0 1 0 0 0 0
## 5 0 0 0 0 0 0 0 1 0 0 0
## 6 0 2 1 0 0 0 0 0 1 1 0
## 7 0 0 0 0 0 0 0 0 0 1 0
## 146.99 147.59 147.72 149 149.59 149.95 149.97 149.98 149.99 150 150.87
## 1 0 0 0 0 0 0 0 1 4 10 1
## 2 0 0 0 1 0 1 0 1 1 3 0
## 3 1 1 1 3 0 3 2 0 9 16 0
## 4 0 0 0 0 0 0 0 1 4 11 0
## 5 0 0 0 1 0 0 0 0 2 1 0
## 6 0 0 0 4 1 1 0 0 3 20 0
## 7 0 0 0 2 0 1 0 0 0 1 0
## 150.99 152 153.95 153.99 154 154.99 155 155.99 157 158.99 159 159.93
## 1 0 0 0 0 0 1 1 0 0 0 0 0
## 2 0 0 0 0 0 0 1 0 0 0 0 0
## 3 2 1 1 1 3 0 4 0 1 1 0 1
## 4 0 0 0 0 0 0 0 1 0 0 1 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 2 0 0 0 0 0
## 7 0 0 0 0 0 0 1 0 0 0 0 0
## 159.94 159.95 159.99 160 160.57 162 164 164.99 165 167.38 168 169 169.95
## 1 0 0 2 0 0 0 0 0 2 1 1 0 0
## 2 0 1 0 0 0 0 0 0 2 0 0 0 1
## 3 1 1 3 6 0 2 1 4 5 0 0 3 0
## 4 0 0 1 1 0 0 0 0 1 0 0 0 0
## 5 0 0 0 1 0 0 0 0 0 0 0 0 0
## 6 0 1 6 3 1 0 0 0 2 0 1 0 0
## 7 0 0 0 2 0 0 0 0 0 0 0 0 0
## 169.98 169.99 170 171 171.95 172 173 174 174.95 174.99 175 176.27 177.99
## 1 0 2 1 0 0 0 0 1 0 0 2 0 1
## 2 0 0 1 0 0 0 0 0 0 1 2 0 0
## 3 1 2 2 1 0 2 1 0 0 4 11 0 0
## 4 0 0 0 0 0 0 0 0 1 0 4 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 1 1 0 1 0 0 0 0 2 6 1 0
## 7 0 0 0 0 0 0 0 0 0 0 5 0 0
## 178.99 179 179.95 179.96 179.99 180 181 182 182.77 184.5 184.95 184.99
## 1 0 0 0 0 1 1 0 0 1 0 0 0
## 2 0 0 0 0 0 3 0 0 0 0 0 0
## 3 0 5 2 0 4 7 0 1 0 0 0 1
## 4 0 1 1 0 2 2 0 0 0 1 1 0
## 5 0 0 0 0 1 0 0 0 0 0 0 1
## 6 1 3 0 1 3 1 1 0 0 0 0 1
## 7 0 0 0 0 0 3 0 0 0 0 0 0
## 185 185.49 186 187 187.5 187.89 187.99 188 188.88 188.99 189 189.85
## 1 2 0 1 0 0 0 0 1 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 1 1
## 4 4 0 0 0 3 0 0 0 0 1 1 0
## 5 1 0 0 1 0 0 0 0 0 0 0 0
## 6 1 1 0 0 0 1 0 0 1 0 0 0
## 7 1 0 0 0 0 0 2 0 0 0 0 0
## 189.95 189.99 190 190.45 190.99 193 193.15 194 194.29 194.85 194.95 195
## 1 0 0 0 0 1 0 1 0 0 0 0 1
## 2 0 0 0 1 0 0 0 0 0 0 0 0
## 3 1 4 1 1 0 1 0 1 0 1 1 1
## 4 0 1 1 0 0 0 0 0 0 0 0 2
## 5 0 1 0 0 0 0 0 0 0 0 0 0
## 6 0 3 2 0 0 0 0 0 1 0 0 1
## 7 0 0 0 0 0 0 0 0 0 0 0 1
## 196 196.79 197.97 198 198.98 199 199.69 199.97 199.99 200 200.29 201.99
## 1 0 1 0 0 0 3 0 1 2 3 0 0
## 2 0 0 0 1 0 0 0 0 1 1 0 0
## 3 0 0 0 0 1 1 0 0 10 8 0 0
## 4 1 0 0 0 0 5 1 0 11 16 1 0
## 5 0 0 0 0 0 2 0 0 7 8 0 0
## 6 0 0 0 1 0 6 0 1 9 8 0 0
## 7 0 0 1 0 0 3 0 0 1 5 0 1
## 204 204.95 205 208 208.99 209 209.85 209.9 209.98 209.99 210 210.99
## 1 0 0 0 0 0 0 0 0 0 0 1 0
## 2 0 0 0 0 0 0 0 1 0 0 0 0
## 3 2 1 0 0 0 1 0 0 0 0 1 0
## 4 1 0 0 0 0 2 0 0 0 2 0 1
## 5 0 0 0 0 0 2 0 0 0 0 0 0
## 6 0 0 1 1 1 1 1 0 0 1 3 0
## 7 0 0 0 0 0 0 0 0 1 0 1 0
## 211.5 211.95 212.99 214.95 214.98 214.99 215 215.99 217 218 219 219.85
## 1 0 0 0 1 0 0 1 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 1 1 0 0 0 1 1 0 0 0 1
## 4 0 0 0 0 0 1 3 0 0 1 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 1 0 1 0 1 0 2 1 0 0 1 0
## 7 0 0 0 0 0 0 1 0 1 0 0 0
## 219.95 219.99 220 222.72 223 224 224.98 224.99 225 227 227.88 227.95
## 1 0 0 1 0 0 1 0 0 1 0 0 0
## 2 0 0 1 0 0 0 0 0 1 1 0 0
## 3 0 0 2 0 0 0 0 0 1 0 0 1
## 4 1 5 6 0 0 0 1 1 8 0 0 0
## 5 0 0 0 0 0 0 0 0 1 0 0 0
## 6 0 2 1 0 0 0 0 0 2 0 1 0
## 7 0 0 0 1 1 0 0 0 6 0 0 0
## 228.59 228.88 229 229.95 229.97 229.98 229.99 230 232.99 234 234.99 235
## 1 0 0 1 1 0 0 1 1 1 0 0 0
## 2 0 0 1 0 1 0 1 0 0 0 0 1
## 3 1 0 0 0 0 0 1 0 0 0 1 1
## 4 0 1 2 0 0 1 2 0 0 1 0 1
## 5 0 0 4 0 0 1 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 2 0 0 0 1
## 7 0 0 1 0 0 0 0 2 0 0 0 3
## 235.99 237 237.99 238 238.8 239 239.88 239.95 239.99 240 241.88 242
## 1 0 0 0 0 0 0 0 0 1 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 1 0 0 1 0 0 0
## 4 1 0 1 1 0 1 2 1 2 2 0 0
## 5 0 0 0 0 0 0 0 0 0 1 0 1
## 6 0 0 0 0 0 1 0 0 2 1 1 0
## 7 0 1 0 0 1 1 0 0 1 0 0 0
## 244.95 244.96 244.97 245 245.19 246 248 248.18 249 249.59 249.95 249.97
## 1 0 0 0 0 0 0 0 0 3 0 0 0
## 2 0 0 0 1 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 1 3
## 4 1 1 0 0 1 1 1 0 2 1 1 1
## 5 0 0 0 2 0 0 0 0 1 0 0 0
## 6 0 0 1 0 0 0 0 0 2 0 1 0
## 7 0 0 0 1 0 0 0 1 3 0 0 0
## 249.98 249.99 250 252.88 252.99 254.99 255 256.24 257.83 258.88 258.98
## 1 0 3 4 0 0 0 1 0 0 0 0
## 2 0 0 2 0 0 0 0 0 0 0 0
## 3 0 0 4 0 0 0 1 0 0 0 0
## 4 0 12 15 0 1 1 2 0 0 0 0
## 5 2 1 9 0 0 0 1 1 1 0 0
## 6 0 4 5 1 0 0 1 0 0 1 2
## 7 0 0 6 0 0 0 0 0 0 0 0
## 259 259.95 259.99 260 261.99 263.99 264.95 264.99 265 265.99 266.05 269
## 1 1 0 1 1 0 0 0 0 1 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 2 0 3 1 1 1 1 1 2 0 0 0
## 5 0 0 1 2 0 0 0 0 2 0 1 0
## 6 1 0 3 1 0 0 0 0 1 1 0 0
## 7 0 1 0 1 0 0 0 1 0 0 0 2
## 269.85 269.94 269.95 269.99 270 270.99 271 274 274.99 275 276.99 279
## 1 0 0 0 0 0 0 0 0 0 1 1 1
## 2 0 0 0 1 0 0 0 0 0 1 0 0
## 3 0 1 0 0 0 0 0 0 0 3 0 1
## 4 0 0 1 2 1 0 0 0 0 7 1 0
## 5 1 0 0 0 0 1 0 1 1 1 0 2
## 6 0 0 0 0 0 0 1 0 0 4 0 1
## 7 0 0 0 0 0 0 0 0 0 2 0 0
## 279.5 279.95 279.99 280 280.99 284 284.99 285 288 289 289.79 289.95
## 1 0 0 0 2 0 0 0 1 0 1 0 0
## 2 0 2 0 0 0 0 0 0 0 0 0 1
## 3 0 0 1 1 0 0 0 0 1 0 0 0
## 4 1 0 6 3 1 1 0 2 0 1 0 0
## 5 0 0 4 2 0 0 0 0 1 0 1 0
## 6 1 0 1 2 0 0 0 2 0 1 0 0
## 7 0 0 1 2 0 0 2 3 0 2 0 1
## 289.98 289.99 290 291.99 292.5 294.99 295 295.95 298 298.97 299 299.95
## 1 0 0 0 0 0 0 1 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 1 0 0 0 1 0 0 0 1 0 0
## 4 1 1 1 1 0 0 3 0 1 0 6 1
## 5 0 0 3 0 1 1 1 0 0 0 3 0
## 6 0 1 3 0 0 0 1 0 1 0 0 1
## 7 0 0 1 0 0 0 1 1 0 0 4 0
## 299.98 299.99 300 303.67 303.99 304.89 305 308 309.95 309.98 309.99 310
## 1 0 3 8 0 0 0 0 0 0 1 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 3 0 0 0 0 0 0 0 0 1
## 4 0 3 9 0 1 1 2 0 0 0 2 2
## 5 1 4 11 0 0 0 1 0 0 0 0 2
## 6 0 2 4 0 0 0 0 0 0 0 0 1
## 7 0 3 7 1 0 0 0 1 1 0 0 1
## 314.99 315 318 319 319.85 319.95 319.98 319.99 320 320.99 322.99 324.9
## 1 0 0 0 3 0 0 0 2 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 1 0 0 0 0 0 0 0 0 0 0
## 4 2 2 0 0 0 1 0 1 3 0 0 1
## 5 0 0 1 2 1 2 0 3 4 2 1 0
## 6 0 1 0 0 0 0 0 0 1 0 0 0
## 7 0 2 0 0 0 0 1 1 0 0 0 0
## 324.99 325 327.58 329 329.99 330 331.99 332.5 334 334.95 334.99 335 339
## 1 0 1 0 0 1 0 0 0 0 0 1 0 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 1 0 0 0 0 0 0 0
## 4 2 2 0 1 0 0 0 0 0 0 0 0 2
## 5 1 4 0 0 4 0 0 0 0 0 1 0 3
## 6 0 0 0 0 0 0 0 0 0 1 0 0 0
## 7 0 6 1 3 5 0 1 1 1 0 0 1 2
## 339.5 339.98 339.99 340 344 344.95 345 346 347 347.24 348.6 349 349.95
## 1 0 0 0 1 0 0 0 0 1 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 1 0 0 0 0 0 0 0 0 0
## 4 0 0 0 1 2 0 1 0 0 0 0 0 0
## 5 0 0 1 0 0 1 0 1 0 1 0 3 1
## 6 0 0 1 0 0 0 0 0 0 0 1 0 0
## 7 1 1 1 1 0 0 2 0 0 0 0 2 1
## 349.99 350 350.25 351 358.24 358.87 359 359.99 360 360.24 367.97 369.99
## 1 1 3 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 1 2 0 0 0 0 0 0 0 0 0 0
## 4 1 3 0 0 0 0 0 0 0 0 2 0
## 5 4 9 0 0 1 0 0 1 1 1 0 2
## 6 1 5 0 1 0 1 0 0 0 0 0 0
## 7 3 12 1 0 0 0 1 1 0 0 0 0
## 370 374.95 374.99 375 375.99 376 379 379.95 379.99 380 384.99 385 387
## 1 0 0 0 2 0 0 0 0 0 1 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 2 0 0 0 0 0 1 0 0 0
## 5 1 2 1 3 1 0 3 0 4 2 1 0 0
## 6 1 0 0 1 0 0 0 0 1 0 0 1 0
## 7 1 0 0 5 0 1 0 1 2 2 0 2 1
## 387.45 388.3 388.99 389 389.99 393 394.99 395 396 397.75 398.99 399
## 1 0 0 0 1 0 0 0 1 0 0 0 2
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 1 0 1 1 0 0 0
## 4 0 0 0 0 0 0 0 1 0 0 0 0
## 5 0 0 1 1 6 0 1 1 0 0 2 4
## 6 1 1 0 0 0 0 0 0 0 1 1 0
## 7 0 0 0 0 2 0 1 1 0 0 0 1
## 399.94 399.95 399.99 400 404.99 406 408 408.6 409.99 410 415 417 419
## 1 0 0 0 0 0 0 0 1 0 0 1 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 1 0 0 0 0 0 1 0
## 4 0 0 1 5 0 0 0 0 0 1 0 0 0
## 5 1 2 6 10 1 0 1 0 0 1 3 0 2
## 6 0 0 1 3 0 0 0 0 1 0 0 0 0
## 7 0 0 5 5 0 0 0 0 0 0 0 0 0
## 419.95 419.99 420 424.55 424.65 424.95 424.99 425 425.99 426.3 426.99
## 1 0 1 0 0 0 0 0 0 1 1 0
## 2 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 1 0 0 0
## 4 0 1 0 0 0 0 0 1 0 0 0
## 5 0 0 1 1 1 1 1 3 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0
## 7 1 1 1 0 0 0 0 1 0 0 1
## 429 429.95 429.99 430 438 438.99 439 439.98 439.99 440 443.09 444.99 445
## 1 0 0 0 0 0 0 0 1 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 1
## 5 1 1 2 2 1 1 1 1 4 1 1 1 1
## 6 1 0 1 0 0 0 0 0 0 0 0 0 0
## 7 0 0 1 1 0 0 0 0 1 0 0 0 0
## 445.95 449 449.95 449.99 450 454 454.68 455 458 459 459.95 459.99 460
## 1 0 0 0 0 2 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 2 0 0 0 0 0 0 0 0
## 5 0 2 0 3 8 1 1 1 0 1 1 1 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0
## 7 1 1 1 3 2 0 0 0 1 0 0 1 2
## 462.89 463.26 465.99 469 469.99 470 473.6 475 479.99 480 485 489.99 490
## 1 0 0 0 0 0 1 1 0 0 1 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 1 0 0 0 0 0 0 1 0 0 0 0
## 5 1 0 2 0 2 0 0 2 0 3 1 1 1
## 6 0 0 0 0 0 0 0 1 0 0 0 0 0
## 7 0 0 0 1 1 0 0 1 0 0 1 0 0
## 490.95 494.5 495.49 495.99 498.88 499 499.95 499.99 500 509 509.99 510
## 1 0 0 0 0 0 0 0 0 2 0 0 0
## 2 0 0 0 0 0 1 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 1 0 0 0 0 0 0 2 0 0 0
## 5 1 0 1 1 0 4 1 5 8 1 1 1
## 6 0 0 0 0 0 0 0 1 0 0 0 0
## 7 0 0 0 0 2 2 0 5 3 1 0 1
## 514.95 515 517.89 520 520.9 525 528 529 529.95 529.99 535 539.95 540
## 1 0 0 0 0 1 0 0 0 0 0 1 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 1 0 0 0 0 0 0 0
## 4 0 0 0 1 0 0 0 0 0 0 0 0 1
## 5 1 1 1 1 0 3 1 1 1 1 0 1 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 1 0 0 0 1 0 0 0
## 544.49 549 549.9 549.95 549.99 550 554.77 558.17 559 559.99 560 561.53
## 1 0 0 0 0 0 1 1 0 0 0 0 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 1 0 0 0 0 0 0 0 0 1 0 0
## 5 0 2 1 1 5 10 0 1 1 2 3 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## 7 0 0 0 0 1 2 0 0 0 1 0 0
## 565.95 569 573.74 575 579.99 585.99 588.18 589 589.99 590 595 598.98 599
## 1 0 0 0 0 0 0 0 0 0 1 1 0 1
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 1 0 0 0 2 0 0 0 0 0 0
## 5 1 0 0 2 3 1 0 1 1 1 1 1 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0
## 7 0 1 0 2 1 0 0 0 0 0 1 0 0
## 599.99 600 609.99 614.99 615.99 619 619.99 624.99 625 629 630 634.99 639
## 1 2 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 1 0 0 0 0 0 0 0 0 0 0 0
## 5 1 1 0 1 1 1 1 1 1 1 1 1 2
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0
## 7 5 0 1 1 0 0 0 0 0 0 0 0 0
## 639.99 640 645 645.99 648 649.95 649.99 650 659.49 660 670 675 679.95
## 1 1 1 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 2 1 0 0 0 0 0
## 5 2 0 1 1 2 1 1 3 1 1 1 1 1
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0
## 7 1 0 0 0 0 0 0 1 0 0 0 0 0
## 679.99 680 689.99 695 699 699.95 700 710 720.12 729.99 730 740 749
## 1 1 0 0 0 0 0 2 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 1 0 0 0 0 0 0
## 4 0 0 0 1 0 1 0 0 0 0 0 0 0
## 5 1 1 0 0 1 0 0 1 0 2 1 1 2
## 6 0 0 0 0 0 0 0 0 1 0 0 0 0
## 7 0 0 1 0 0 0 0 0 0 3 0 0 0
## 749.95 749.99 750 785 789 789.99 795 795.99 798 799 799.99 800 820
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 2 1 2 1 1 1 1 1 1 1 0 2 1
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0 0 0 1 0 0
## 829.99 879.99 899.99 900 939 948.98 999 999.99 .entropy .knt
## 1 0 0 0 0 0 0 0 0 4.811470 285
## 2 0 0 0 0 0 0 0 0 4.359548 314
## 3 0 0 0 0 0 0 0 0 4.700760 442
## 4 0 0 0 0 0 0 0 0 4.798635 434
## 5 1 1 1 1 1 0 0 0 5.114880 490
## 6 0 0 0 0 0 0 1 1 4.663055 393
## 7 0 0 0 0 0 1 0 0 4.703751 299
## [1] "Category: Unknown"
## [1] "max distance(1.0000) pair:"
## UniqueID startprice prdline.my
## 5 10005 199.99 Unknown
## 130 10130 100.00 Unknown
## descr.my
## 5 Please feel free to buy. All product have been thoroughly inspected, cleaned and tested to be 100%
## 130 New - Open Box. Charger included.
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 5 0 0 0 0.0000000 0 0 0
## 130 0 0 0 0.8180361 0 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 5 0 0 0 0 0 0
## 130 0 0 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID startprice prdline.my
## 1029 11029 108 Unknown
## 1077 11077 108 Unknown
## descr.my
## 1029 A device listed in near mint used cosmetic condition with light blemishes from use. Housing &
## 1077 A device listed in near mint used cosmetic condition with light blemishes from use. Housing &
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 1029 0.220126 0.5801286 0 0 0 0 0
## 1077 0.220126 0.5801286 0 0 0 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 1029 0 0 0 0 0 0
## 1077 0 0 0 0 0 0
## [1] "Category: iPad 1"
## [1] "max distance(1.0000) pair:"
## UniqueID startprice prdline.my
## 9 10009 0.99 iPad 1
## 13 10013 20.00 iPad 1
## descr.my
## 9
## 13 GOOD CONDITION. CLEAN ICLOUD. NO LOCKS. CLEAN IMEI. This tablet has been fully tested and works
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 9 0.000000 0 0 0 0.0000000 0 0
## 13 0.220126 0 0 0 0.3412301 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 9 0 0.000000 0 0 0 0
## 13 0 0.340566 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID startprice prdline.my
## 471 10471 110 iPad 1
## 1202 11202 119 iPad 1
## descr.my
## 471 Used Apple Ipad 64 gig 1st generation in Great working condition and 100% functional SIM card AT&T
## 1202 Used Apple Ipad 64 gig 1st generation in Great working condition and 100% functional SIM card AT&T
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 471 0.1862605 0.245439 0 0 0 0.2705847 0
## 1202 0.1862605 0.245439 0 0 0 0.2705847 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 471 0.3392152 0.2881712 0 0 0 0
## 1202 0.3392152 0.2881712 0 0 0 0
## [1] "Category: iPad 2"
## [1] "max distance(1.0000) pair:"
## UniqueID startprice prdline.my
## 1 10001 159.99 iPad 2
## 2 10002 0.99 iPad 2
## descr.my
## 1 iPad is in 8.5+ out of 10 cosmetic condition!
## 2 Previously used, please read description. May show signs of use such as scratches to the screen and
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 1 0.8071287 0.0000000 0.0000000 0 0 1.172534 0.0000000
## 2 0.0000000 0.5801286 0.2923374 0 0 0.000000 0.3309884
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID startprice prdline.my
## 132 10132 119.99 iPad 2
## 2382 12384 189.95 iPad 2
## descr.my
## 132 Overall good condition. Some wear from use. Scratches/ scuffs/ nicks/ scrapes on unit housing back,
## 2382 Device is in GOOD used cosmetic condition with normal scratches & wear, engravement on the housing.
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad
## 132 0.2017822 0.2658923 0.2679759 0 0.3127942 0
## 2382 0.2421386 0.3190707 0.3215711 0 0.3753531 0
## D.T.screen D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 132 0 0 0 0 0 0 0
## 2382 0 0 0 0 0 0 0
## [1] "Category: iPad 3+"
## [1] "max distance(1.0000) pair:"
## UniqueID startprice prdline.my
## 3 10003 199.99 iPad 3+
## 11 10011 199.99 iPad 3+
## descr.my
## 3
## 11 good condition, minor wear and tear on body some light scratches on screen. functions great.
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 3 0.000000 0 0.0000000 0 0.0000000 0 0.0000000
## 11 0.220126 0 0.2923374 0 0.3412301 0 0.3309884
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 3 0.0000000 0 0 0 0 0
## 11 0.4008907 0 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID startprice prdline.my
## 40 10040 159.99 iPad 3+
## 1602 11603 0.99 iPad 3+
## descr.my
## 40 Item has been professionally tested and inspected. Tests show that all features work correctly. This
## 1602 Work fine iCloud lock
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 40 0 0 0 0 0 0 0
## 1602 0 0 0 0 0 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 40 0 0.4162473 0 0 0 0
## 1602 0 0.9365565 0 0 0 0
## [1] "Category: iPadAir"
## [1] "max distance(1.0000) pair:"
## UniqueID startprice prdline.my
## 16 10016 344.95 iPadAir
## 33 10033 299.98 iPadAir
## descr.my
## 16
## 33 We are selling good quality iPads that have been fully tested by an Apple Certified Technician. The
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 16 0 0 0 0 0.000000 0.0000000 0
## 33 0 0 0 0 0.417059 0.3908446 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 16 0 0 0 0 0 0
## 33 0 0 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID startprice prdline.my
## 44 10044 499.95 iPadAir
## 1233 11233 199.99 iPadAir
## descr.my
## 44 Open Box Units Grade A Condition. Units may contain minor cosmetic imperfections.
## 1233 MINT CONDITION!
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 44 0.220126 0 0 0 0 0 0
## 1233 1.210693 0 0 0 0 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 44 0 0 0 0 0 0
## 1233 0 0 0 0 0 0
## [1] "Category: iPadmini"
## [1] "max distance(1.0000) pair:"
## UniqueID startprice prdline.my
## 7 10007 100 iPadmini
## 76 10076 130 iPadmini
## descr.my
## 7
## 76 Works perfectly, NOT iCloud locked, 1 owner. It is in not in very good condition, but works
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 7 0.0000000 0 0 0 0.0000000 0 0
## 76 0.3026733 0 0 0 0.4691913 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 7 0 0.0000000 0 0 0 0
## 76 0 0.9365565 0 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID startprice prdline.my
## 491 10491 5 iPadmini
## 1753 11754 79 iPadmini
## descr.my
## 491 Cracked screen, flaw is shown in picture, everything is fully functional and
## 1753 Shows Apple ID locked, password required on activation screen. Unknown imei and storage space.
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 491 0 0 0 0 0 0 0.4551091
## 1753 0 0 0 0 0 0 0.4045414
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 491 0 0 0 0 0 0
## 1753 0 0 0 0 0 0
## [1] "Category: iPadmini 2+"
## [1] "max distance(1.0000) pair:"
## UniqueID startprice prdline.my
## 4 10004 235.00 iPadmini 2+
## 18 10018 209.98 iPadmini 2+
## descr.my
## 4
## 18 We are selling good quality iPads that have been fully tested by an Apple Certified Technician. The
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 4 0 0 0 0 0.000000 0.0000000 0
## 18 0 0 0 0 0.417059 0.3908446 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 4 0 0 0 0 0 0
## 18 0 0 0 0 0 0
## [1] "min distance(0.0000) pair:"
## UniqueID startprice prdline.my descr.my D.T.condit D.T.use D.T.scratch
## 4 10004 235 iPadmini 2+ 0 0 0
## 6 10006 175 iPadmini 2+ 0 0 0
## D.T.new D.T.good D.T.ipad D.T.screen D.T.great D.T.work D.T.excel
## 4 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## D.P.http D.P.mini D.P.air
## 4 0 0 0
## 6 0 0 0
## [1] "glb_allobs_df$prdline.my$.clusterid Entropy: NA (NA pct)"
## prdline.my.clusterid 0.01 0.1 0.45 0.5 0.98 0.99 1 1.99 2.99 3.99 4.69
## 1 Unknown_1 NA NA NA NA NA 19 1 NA 1 1 1
## 2 Unknown_2 1 NA NA NA NA 3 NA NA NA NA NA
## 3 Unknown_3 NA NA NA NA NA 3 NA NA NA NA NA
## 4 iPad 1_1 2 NA NA NA NA 17 6 NA NA NA NA
## 5 iPad 1_2 NA NA NA NA NA 2 NA NA NA NA NA
## 6 iPad 1_3 1 NA NA NA NA 3 NA NA NA NA NA
## 7 iPad 1_4 NA NA NA NA NA 4 NA NA NA NA NA
## 8 iPad 2_1 4 2 NA NA NA 28 4 NA NA NA NA
## 9 iPad 2_2 3 NA NA NA NA 6 NA NA NA NA NA
## 10 iPad 2_3 NA NA NA NA NA 1 NA NA NA NA NA
## 11 iPad 2_4 2 NA NA NA NA 3 NA NA NA NA NA
## 12 iPad 2_5 1 NA NA NA NA NA NA NA NA NA NA
## 13 iPad 3+_1 3 NA NA 1 NA 18 3 NA NA NA NA
## 14 iPad 3+_2 1 NA NA NA NA 2 1 NA NA NA NA
## 15 iPad 3+_3 1 NA NA NA NA 4 NA NA NA NA NA
## 16 iPad 3+_4 1 NA NA NA NA 3 NA NA NA NA NA
## 17 iPadAir_1 1 NA NA NA NA 19 5 NA NA NA NA
## 18 iPadAir_2 NA NA NA NA NA 6 4 1 NA NA NA
## 19 iPadAir_3 NA NA NA NA NA 4 NA NA NA NA NA
## 20 iPadAir_4 NA NA NA NA NA 2 NA NA NA NA NA
## 21 iPadmini 2+_1 2 NA 1 NA NA 12 1 NA NA NA NA
## 22 iPadmini 2+_2 NA NA NA NA NA 3 NA NA NA NA NA
## 23 iPadmini 2+_3 1 NA NA NA NA 5 1 NA NA NA NA
## 24 iPadmini_1 2 NA NA NA NA 23 3 NA NA NA NA
## 25 iPadmini_2 NA NA NA NA 1 4 NA NA NA NA NA
## 26 iPadmini_3 NA NA NA NA NA 2 1 NA NA NA NA
## 27 iPadmini_4 NA NA NA NA NA 4 NA NA NA NA NA
## 28 iPadmini_5 1 NA NA NA NA 1 1 NA NA NA NA
## 4.99 5 5.65 7.99 8 8.99 9 9.5 9.95 9.99 10 10.99 14 14.49 14.99 15
## 1 1 2 1 1 NA 1 NA NA 1 1 1 NA 1 1 1 3
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA 1 NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA 1 1 NA 1 NA NA NA 2 2
## 5 NA NA NA NA NA NA NA NA NA 2 1 NA NA NA NA NA
## 6 NA NA NA NA NA NA NA 1 NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA 1 NA NA NA NA NA NA NA 2 NA NA NA NA NA 2
## 9 NA NA NA NA NA NA NA NA NA 5 NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA 1 NA 1 NA NA NA NA NA 3 4 NA NA NA NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA 1 1 NA NA NA NA 1 NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 NA NA NA NA NA NA 1 NA NA NA NA NA NA NA NA NA
## 18 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA 1 NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA NA NA NA 1 NA 1 NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA 1 NA NA NA NA NA NA NA 1 NA NA NA NA NA NA
## 25 NA 1 NA NA NA NA NA NA NA NA 1 1 NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA 1 NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA 1 NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17.75 19.5 19.95 19.99 20 24.99 25 28 28.75 29.95 29.99 30 30.99 32.95
## 1 1 NA NA 1 NA NA 4 1 1 NA NA 1 NA 1
## 2 NA NA NA NA 1 1 NA NA NA NA NA NA 1 NA
## 3 NA NA NA NA 1 NA NA NA NA NA NA NA NA NA
## 4 NA NA 1 3 1 NA 2 NA NA 1 5 3 NA NA
## 5 NA NA NA NA 1 NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA 1 NA NA NA NA NA NA NA
## 7 NA NA NA NA 1 NA NA NA NA NA NA NA NA NA
## 8 NA NA 1 NA 1 NA 1 NA NA 1 1 2 NA NA
## 9 NA NA NA 1 NA NA NA NA NA NA NA 1 NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA 1 NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA 1 NA 1 NA NA NA NA NA NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA 1 NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 NA NA NA NA 1 1 1 NA NA NA NA NA NA NA
## 18 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA NA 2 NA NA NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA 1 NA 1 1 NA 2 NA NA NA 1 2 NA NA
## 25 NA NA NA NA 2 NA 1 NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 33 35 36.95 37.98 38.99 39 39.99 40 41 42 43.2 44.99 45 48 48.99 49
## 1 NA 1 NA 1 NA NA 2 2 1 NA 1 NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA 1 1 NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 1 1 3 NA NA NA NA 3 NA 1 NA NA 1 1 NA NA
## 5 NA NA NA NA NA NA 1 1 NA NA NA NA 1 NA NA NA
## 6 NA NA NA NA NA NA NA 1 NA NA NA NA NA NA 1 NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA 4 NA NA NA NA 1 NA NA 1
## 9 NA NA NA NA NA NA 1 NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA 1 NA NA NA 1 NA NA NA NA NA NA NA NA NA NA
## 14 NA NA NA NA 1 NA NA NA NA NA NA NA 1 NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1
## 18 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA NA NA NA 1 NA 1 NA NA 3 NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 49.49 49.95 49.99 50 51.99 52.25 52.99 54.99 55 55.66 56 57.5 58 59
## 1 NA NA NA 3 1 1 NA NA NA NA NA 1 1 NA
## 2 NA NA NA 3 NA NA NA NA NA NA NA NA NA NA
## 3 NA 1 1 1 NA NA NA NA NA NA NA NA NA NA
## 4 1 NA 1 19 NA NA 1 1 4 NA NA NA 2 NA
## 5 NA NA NA 1 NA NA NA NA NA 1 1 NA NA NA
## 6 NA NA 2 2 NA NA NA NA 1 NA NA NA NA NA
## 7 NA NA 1 NA NA NA NA NA NA NA NA NA NA 1
## 8 NA NA 2 3 NA NA NA NA NA NA NA NA NA NA
## 9 NA NA 2 2 NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA 1 NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA 2 2 NA NA NA NA NA NA NA NA NA NA
## 14 NA NA NA 1 NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA 1 NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 NA NA 1 4 NA NA NA NA NA NA NA NA NA NA
## 18 NA NA 1 NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA NA 1
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA 1 NA NA NA NA NA NA NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA 6 NA NA NA NA NA NA NA NA NA NA
## 25 NA NA NA 2 NA NA NA NA NA NA NA NA NA NA
## 26 NA 1 NA 2 NA NA NA NA NA NA NA NA NA NA
## 27 NA NA 1 NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 59.95 59.99 60 62 63 64.99 65 66.99 69 69.5 69.69 69.95 69.99 70 70.99
## 1 1 1 NA NA 1 NA NA NA 1 NA NA NA NA 1 NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA 1 1
## 3 NA NA NA NA NA NA 1 NA NA NA NA NA NA NA NA
## 4 NA 1 NA 2 NA NA 2 NA NA NA NA NA 2 6 NA
## 5 NA NA 1 NA NA NA 1 NA 1 NA NA NA NA NA NA
## 6 NA NA NA NA NA 1 NA NA 1 NA NA NA NA 1 NA
## 7 NA NA 1 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA 2 NA NA NA NA 1 NA NA NA NA 1 1 3 NA
## 9 NA NA 1 NA NA NA 1 1 NA 1 NA NA 1 NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA 1 NA NA NA 1 NA NA NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 NA NA 1 NA NA NA NA NA NA NA NA NA NA NA NA
## 18 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA 1 3 1 NA NA NA NA NA NA NA NA NA 1 NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA NA 1 NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 71 71.99 72 74 74.5 74.95 74.99 75 76 78 79 79.94 79.95 79.97 79.99 80
## 1 NA NA NA NA NA NA NA 2 NA NA NA NA 2 NA NA 1
## 2 NA NA NA NA NA NA NA 1 NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA 1 NA NA NA NA NA NA 1
## 4 NA NA 1 NA NA NA 1 9 NA NA 3 NA NA NA NA 7
## 5 NA NA NA 1 1 NA NA NA NA NA 1 NA NA NA 2 3
## 6 NA NA NA NA NA NA NA 1 NA NA NA 1 NA NA NA 2
## 7 NA NA NA NA NA NA NA NA NA 1 NA NA NA NA 3 NA
## 8 NA 1 1 1 NA NA 1 6 NA NA 1 NA NA 1 NA 5
## 9 NA NA NA NA NA NA 1 1 NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA 2
## 14 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1
## 15 NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1 NA
## 16 NA NA NA NA NA NA NA 1 NA NA NA NA NA NA NA NA
## 17 NA NA NA NA NA NA NA 2 NA NA NA NA NA NA NA 1
## 18 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA NA NA NA NA NA NA NA 1 NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA NA 1 NA 3 NA NA NA NA NA NA 1 NA
## 25 NA NA NA NA NA NA NA 1 NA NA 1 NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA 1 NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA 1 1 NA NA NA NA NA NA NA NA
## 82.95 82.98 84.99 85 85.95 87 89 89.5 89.95 89.99 90 91 92 92.14 92.49
## 1 NA NA NA NA NA 1 NA NA NA NA NA 1 NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
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## 4 1 1 3 1 1 NA 2 NA NA NA 7 NA NA 2 NA
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## 93 94.99 95 96 97.5 98 99 99.46 99.75 99.94 99.95 99.97 99.98 99.99 100
## 1 NA NA NA NA NA NA 1 1 NA NA NA NA NA NA 5
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## 1 NA NA NA NA NA NA NA NA NA NA NA 1 NA NA
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## 1 NA NA NA NA 1 NA NA NA 1 NA 1 1
## 2 NA NA NA NA NA NA NA NA NA NA NA NA
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## 4 NA NA NA NA NA 1 NA 1 NA NA NA NA
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## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 119 119.88 119.95 119.98 119.99 120 120.02 121 124 124.95 124.99 125
## 1 NA NA NA NA 1 2 NA NA NA NA NA 1
## 2 NA NA NA NA NA 1 NA NA NA NA 1 NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA 1 1 1 NA NA 2 1 1
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## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA 1 NA NA NA NA NA NA NA NA
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## 1 1 NA NA NA NA NA NA NA NA NA NA 1
## 2 NA NA NA NA NA NA NA NA NA NA NA NA
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## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 139 139.5 139.98 139.99 140 141.09 142.25 142.49 144.5 144.95 144.99
## 1 NA NA NA 1 NA NA NA NA NA NA NA
## 2 1 NA NA NA NA NA NA NA NA NA NA
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## 27 NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA
## 145 145.49 146.99 147.59 147.72 149 149.59 149.95 149.97 149.98 149.99
## 1 1 NA NA NA NA NA NA NA NA 1 1
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## 3 NA NA NA NA NA NA NA NA NA NA 1
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## 28 NA NA NA NA NA NA NA NA NA NA NA
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## 1 7 1 NA NA NA NA NA NA NA NA NA NA
## 2 2 NA NA NA NA NA NA 1 1 NA NA NA
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## 1 NA NA NA NA NA NA NA NA NA NA NA 1
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## 1 1 NA NA NA 1 NA NA NA NA NA 1 NA NA
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## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 175 176.27 177.99 178.99 179 179.95 179.96 179.99 180 181 182 182.77
## 1 1 NA 1 NA NA NA NA 1 1 NA NA NA
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## 1 NA NA NA 1 NA NA NA NA NA NA NA NA
## 2 NA NA NA 1 NA NA NA NA NA NA NA NA
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## 1 NA NA NA NA NA NA NA NA NA 1 NA NA
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## 1 NA NA NA NA 1 NA NA NA 1 NA NA 2
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## 28 NA NA NA NA NA NA 1 NA NA NA NA 1
## 200 200.29 201.99 204 204.95 205 208 208.99 209 209.85 209.9 209.98
## 1 2 NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA
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## 1 NA 1 NA NA NA NA 1 NA NA 1 NA
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## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 408 408.6 409.99 410 415 417 419 419.95 419.99 420 424.55 424.65 424.95
## 1 NA 1 NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA 1 NA NA NA 1 NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA 1 NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA NA NA 1 NA NA NA NA
## 14 NA NA NA 1 NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 1 NA NA 1 3 NA 2 NA NA 1 1 1 1
## 18 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA NA NA NA NA 1 NA NA NA
## 22 NA NA NA NA NA NA NA 1 NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA 1 NA NA NA NA
## 24 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA 1 NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 424.99 425 425.99 426.3 426.99 429 429.95 429.99 430 438 438.99 439
## 1 NA NA 1 1 NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA 1 NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA 1 NA NA NA NA NA NA NA NA NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA
## 17 NA 3 NA NA NA 1 1 1 2 1 1 NA
## 18 1 NA NA NA NA NA NA NA NA NA NA 1
## 19 NA NA NA NA NA NA NA 1 NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA 1 NA NA NA NA NA 1 NA NA NA NA
## 22 NA NA NA NA 1 NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA 1 NA NA NA
## 24 NA NA NA NA NA NA NA 1 NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA 1 NA NA NA NA NA NA
## 439.98 439.99 440 443.09 444.99 445 445.95 449 449.95 449.99 450 454
## 1 NA NA NA NA NA NA NA NA NA NA 2 NA
## 2 1 NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA NA NA NA NA 2 NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA 1 NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA
## 17 1 4 1 1 1 1 NA 1 NA 2 8 1
## 18 NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA 1 NA NA
## 20 NA NA NA NA NA NA NA 1 NA NA NA NA
## 21 NA 1 NA NA NA NA 1 1 NA 2 2 NA
## 22 NA NA NA NA NA NA NA NA NA 1 NA NA
## 23 NA NA NA NA NA NA NA NA 1 NA NA NA
## 24 NA NA NA NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 454.68 455 458 459 459.95 459.99 460 462.89 463.26 465.99 469 469.99
## 1 NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA NA NA NA NA NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA 1 NA NA NA
## 17 1 1 NA 1 1 1 NA 1 NA NA NA 1
## 18 NA NA NA NA NA NA NA NA NA NA NA 1
## 19 NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA 2 NA NA
## 21 NA NA 1 NA NA 1 1 NA NA NA 1 NA
## 22 NA NA NA NA NA NA 1 NA NA NA NA 1
## 23 NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 470 473.6 475 479.99 480 485 489.99 490 490.95 494.5 495.49 495.99
## 1 1 1 NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA 1 NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA 1 NA NA NA NA NA NA NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA 1 NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA
## 17 NA NA 1 NA 1 1 1 1 NA NA 1 1
## 18 NA NA NA NA 1 NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA 1 NA 1 NA NA NA 1 NA NA NA
## 21 NA NA 1 NA NA 1 NA NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA 1 NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 498.88 499 499.95 499.99 500 509 509.99 510 514.95 515 517.89 520 520.9
## 1 NA NA NA NA 2 NA NA NA NA NA NA NA 1
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA 1 NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 14 NA NA NA NA 1 NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA 1 NA NA NA NA NA NA 1 NA
## 17 NA 2 NA 4 7 NA 1 1 NA 1 1 1 NA
## 18 NA 1 NA 1 NA NA NA NA NA NA NA NA NA
## 19 NA 1 1 NA 1 1 NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA 1 NA NA NA NA
## 21 2 2 NA 4 3 1 NA 1 NA NA NA NA NA
## 22 NA NA NA 1 NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA 1 NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 525 528 529 529.95 529.99 535 539.95 540 544.49 549 549.9 549.95 549.99
## 1 NA NA NA NA NA 1 NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 9 1 NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 14 NA NA NA NA NA NA NA 1 1 NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 2 1 1 1 1 NA 1 NA NA 2 1 NA 4
## 18 1 NA NA NA NA NA NA NA NA NA NA 1 NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA 1
## 21 1 NA NA NA 1 NA NA NA NA NA NA NA 1
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 550 554.77 558.17 559 559.99 560 561.53 565.95 569 573.74 575 579.99
## 1 1 1 NA NA NA NA 1 NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA 1 NA NA NA NA 1 NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA
## 17 6 NA NA 1 2 3 NA NA NA NA 2 2
## 18 1 NA 1 NA NA NA NA NA NA NA NA NA
## 19 2 NA NA NA NA NA NA NA NA NA NA NA
## 20 1 NA NA NA NA NA NA 1 NA NA NA 1
## 21 1 NA NA NA 1 NA NA NA 1 NA 2 1
## 22 1 NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 585.99 588.18 589 589.99 590 595 598.98 599 599.99 600 609.99 614.99
## 1 NA NA NA NA 1 1 NA 1 2 NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA 2 NA NA NA NA NA NA NA 1 NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA
## 17 1 NA NA 1 1 1 1 NA 1 1 NA 1
## 18 NA NA 1 NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA 1 NA NA 4 NA 1 1
## 22 NA NA NA NA NA NA NA NA 1 NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA
## 615.99 619 619.99 624.99 625 629 630 634.99 639 639.99 640 645 645.99
## 1 NA NA NA NA NA NA NA NA NA 1 1 NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 1 1 NA 1 1 1 1 1 2 2 NA 1 1
## 18 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA 1 NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA NA NA NA NA 1 NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 648 649.95 649.99 650 659.49 660 670 675 679.95 679.99 680 689.99 695
## 1 NA NA NA NA NA NA NA NA NA 1 NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA 2 1 NA NA NA NA NA NA NA NA 1
## 14 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 2 1 1 3 NA 1 1 1 1 1 1 NA NA
## 18 NA NA NA NA 1 NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA 1 NA NA NA NA NA NA NA 1 NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 699 699.95 700 710 720.12 729.99 730 740 749 749.95 749.99 750 785 789
## 1 NA NA 2 NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA 1 NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 14 NA 1 NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 1 NA NA 1 NA 2 1 1 2 2 1 1 1 1
## 18 NA NA NA NA NA NA NA NA NA NA NA 1 NA NA
## 19 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA 3 NA NA NA NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA 1 NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 789.99 795 795.99 798 799 799.99 800 820 829.99 879.99 899.99 900 939
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 7 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 8 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 9 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 10 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 11 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 12 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 13 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 14 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 15 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 16 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 17 1 1 1 1 1 NA 1 1 1 1 1 1 1
## 18 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 19 NA NA NA NA NA NA 1 NA NA NA NA NA NA
## 20 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 21 NA NA NA NA NA 1 NA NA NA NA NA NA NA
## 22 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 23 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 24 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 25 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 26 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 27 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 28 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 948.98 999 999.99 .entropy .knt
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NA
## 7 NA NA NA NA NA
## 8 NA NA NA NA NA
## 9 NA NA NA NA NA
## 10 NA NA NA NA NA
## 11 NA NA NA NA NA
## 12 NA NA NA NA NA
## 13 NA NA NA NA NA
## 14 NA NA NA NA NA
## 15 NA NA NA NA NA
## 16 NA NA NA NA NA
## 17 NA NA NA NA NA
## 18 NA NA NA NA NA
## 19 NA NA NA NA NA
## 20 NA NA NA NA NA
## 21 1 NA NA NA NA
## 22 NA NA NA NA NA
## 23 NA NA NA NA NA
## 24 NA 1 NA NA NA
## 25 NA NA NA NA NA
## 26 NA NA NA NA NA
## 27 NA NA 1 NA NA
## 28 NA NA NA NA NA# Last call for data modifications
#stop(here") # sav_allobs_df <- glb_allobs_df
# glb_allobs_df[(glb_allobs_df$PropR == 0.75) & (glb_allobs_df$State == "Hawaii"), "PropR.fctr"] <- "N"
# Re-partition
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "select.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 7 manage.missing.data 4 1 88.81 93.67 4.86
## 8 select.features 5 0 93.67 NA NAStep 5.0: select features
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
print(glb_feats_df <- myselect_features(entity_df=glb_trnobs_df,
exclude_vars_as_features=glb_exclude_vars_as_features,
rsp_var=glb_rsp_var))## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## id cor.y
## startprice.log startprice.log 0.7052693713
## sold sold -0.4569767211
## biddable biddable -0.4563275606
## prdline.my.fctr prdline.my.fctr 0.3382438492
## condition.fctr condition.fctr 0.3106762046
## color.fctr color.fctr 0.1677888274
## D.TfIdf.sum.post.stop D.TfIdf.sum.post.stop -0.1296482504
## D.TfIdf.sum.post.stem D.TfIdf.sum.post.stem -0.1254721539
## D.sum.TfIdf D.sum.TfIdf -0.1254721539
## D.npnct24.log D.npnct24.log -0.1250425485
## D.TfIdf.sum.stem.stop.Ratio D.TfIdf.sum.stem.stop.Ratio 0.1196299251
## D.nuppr.log D.nuppr.log -0.1166425681
## D.nchrs.log D.nchrs.log -0.1159829723
## D.nwrds.log D.nwrds.log -0.1098932656
## D.ratio.nstopwrds.nwrds D.ratio.nstopwrds.nwrds 0.1075484534
## D.terms.n.post.stop.log D.terms.n.post.stop.log -0.1073577343
## D.terms.n.post.stem.log D.terms.n.post.stem.log -0.1071833048
## D.nwrds.unq.log D.nwrds.unq.log -0.1071833048
## UniqueID UniqueID 0.1070161471
## idseq.my idseq.my 0.1070161471
## D.nstopwrds.log D.nstopwrds.log -0.1049213237
## D.ratio.sum.TfIdf.nwrds D.ratio.sum.TfIdf.nwrds -0.1049203217
## D.T.screen D.T.screen -0.0931731081
## D.terms.n.post.stop D.terms.n.post.stop -0.0881077263
## D.terms.n.post.stem D.terms.n.post.stem -0.0876424112
## carrier.fctr carrier.fctr 0.0825701842
## .clusterid .clusterid -0.0801748511
## .clusterid.fctr .clusterid.fctr -0.0801748511
## D.npnct13.log D.npnct13.log -0.0787436811
## D.T.work D.T.work -0.0768613740
## D.T.good D.T.good -0.0714102568
## storage.fctr storage.fctr -0.0649793405
## D.npnct09.log D.npnct09.log 0.0625031263
## D.T.ipad D.T.ipad -0.0550850230
## D.npnct03.log D.npnct03.log -0.0516555162
## D.npnct11.log D.npnct11.log -0.0495381784
## D.T.use D.T.use -0.0450788187
## D.T.new D.T.new 0.0423874258
## D.npnct15.log D.npnct15.log -0.0420569179
## D.T.great D.T.great -0.0419289682
## D.npnct10.log D.npnct10.log 0.0399079971
## D.terms.n.stem.stop.Ratio D.terms.n.stem.stop.Ratio 0.0380363992
## D.npnct06.log D.npnct06.log -0.0366796413
## D.npnct16.log D.npnct16.log -0.0338015713
## D.P.air D.P.air 0.0321511783
## D.npnct07.log D.npnct07.log -0.0286463459
## D.npnct28.log D.npnct28.log 0.0283524789
## D.T.scratch D.T.scratch -0.0282866598
## cellular.fctr cellular.fctr 0.0196492177
## D.npnct12.log D.npnct12.log -0.0186354165
## D.T.condit D.T.condit -0.0138032677
## D.npnct18.log D.npnct18.log 0.0119378362
## D.ndgts.log D.ndgts.log -0.0108394471
## D.npnct14.log D.npnct14.log 0.0107251700
## D.T.excel D.T.excel -0.0088931501
## .rnorm .rnorm 0.0076927240
## D.P.mini D.P.mini -0.0068984971
## D.npnct05.log D.npnct05.log 0.0067080843
## D.npnct01.log D.npnct01.log -0.0054440716
## D.npnct08.log D.npnct08.log -0.0004608043
## D.npnct02.log D.npnct02.log NA
## D.npnct04.log D.npnct04.log NA
## D.npnct17.log D.npnct17.log NA
## D.npnct19.log D.npnct19.log NA
## D.npnct20.log D.npnct20.log NA
## D.npnct21.log D.npnct21.log NA
## D.npnct22.log D.npnct22.log NA
## D.npnct23.log D.npnct23.log NA
## D.npnct25.log D.npnct25.log NA
## D.npnct26.log D.npnct26.log NA
## D.npnct27.log D.npnct27.log NA
## D.npnct29.log D.npnct29.log NA
## D.npnct30.log D.npnct30.log NA
## D.P.http D.P.http NA
## exclude.as.feat cor.y.abs
## startprice.log 1 0.7052693713
## sold 1 0.4569767211
## biddable 0 0.4563275606
## prdline.my.fctr 0 0.3382438492
## condition.fctr 0 0.3106762046
## color.fctr 0 0.1677888274
## D.TfIdf.sum.post.stop 0 0.1296482504
## D.TfIdf.sum.post.stem 0 0.1254721539
## D.sum.TfIdf 0 0.1254721539
## D.npnct24.log 0 0.1250425485
## D.TfIdf.sum.stem.stop.Ratio 0 0.1196299251
## D.nuppr.log 0 0.1166425681
## D.nchrs.log 0 0.1159829723
## D.nwrds.log 0 0.1098932656
## D.ratio.nstopwrds.nwrds 0 0.1075484534
## D.terms.n.post.stop.log 0 0.1073577343
## D.terms.n.post.stem.log 0 0.1071833048
## D.nwrds.unq.log 0 0.1071833048
## UniqueID 1 0.1070161471
## idseq.my 0 0.1070161471
## D.nstopwrds.log 0 0.1049213237
## D.ratio.sum.TfIdf.nwrds 0 0.1049203217
## D.T.screen 1 0.0931731081
## D.terms.n.post.stop 0 0.0881077263
## D.terms.n.post.stem 0 0.0876424112
## carrier.fctr 0 0.0825701842
## .clusterid 1 0.0801748511
## .clusterid.fctr 0 0.0801748511
## D.npnct13.log 0 0.0787436811
## D.T.work 1 0.0768613740
## D.T.good 1 0.0714102568
## storage.fctr 0 0.0649793405
## D.npnct09.log 0 0.0625031263
## D.T.ipad 1 0.0550850230
## D.npnct03.log 0 0.0516555162
## D.npnct11.log 0 0.0495381784
## D.T.use 1 0.0450788187
## D.T.new 1 0.0423874258
## D.npnct15.log 0 0.0420569179
## D.T.great 1 0.0419289682
## D.npnct10.log 0 0.0399079971
## D.terms.n.stem.stop.Ratio 0 0.0380363992
## D.npnct06.log 0 0.0366796413
## D.npnct16.log 0 0.0338015713
## D.P.air 1 0.0321511783
## D.npnct07.log 0 0.0286463459
## D.npnct28.log 0 0.0283524789
## D.T.scratch 1 0.0282866598
## cellular.fctr 0 0.0196492177
## D.npnct12.log 0 0.0186354165
## D.T.condit 1 0.0138032677
## D.npnct18.log 0 0.0119378362
## D.ndgts.log 0 0.0108394471
## D.npnct14.log 0 0.0107251700
## D.T.excel 1 0.0088931501
## .rnorm 0 0.0076927240
## D.P.mini 1 0.0068984971
## D.npnct05.log 0 0.0067080843
## D.npnct01.log 0 0.0054440716
## D.npnct08.log 0 0.0004608043
## D.npnct02.log 0 NA
## D.npnct04.log 0 NA
## D.npnct17.log 0 NA
## D.npnct19.log 0 NA
## D.npnct20.log 0 NA
## D.npnct21.log 0 NA
## D.npnct22.log 0 NA
## D.npnct23.log 0 NA
## D.npnct25.log 0 NA
## D.npnct26.log 0 NA
## D.npnct27.log 0 NA
## D.npnct29.log 0 NA
## D.npnct30.log 0 NA
## D.P.http 1 NA# sav_feats_df <- glb_feats_df; glb_feats_df <- sav_feats_df
print(glb_feats_df <- orderBy(~-cor.y,
myfind_cor_features(feats_df=glb_feats_df, obs_df=glb_trnobs_df,
rsp_var=glb_rsp_var)))## [1] "cor(D.TfIdf.sum.post.stem, D.sum.TfIdf)=1.0000"
## [1] "cor(startprice, D.TfIdf.sum.post.stem)=-0.1255"
## [1] "cor(startprice, D.sum.TfIdf)=-0.1255"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.sum.TfIdf as highly correlated with
## D.TfIdf.sum.post.stem## [1] "cor(D.nwrds.unq.log, D.terms.n.post.stem.log)=1.0000"
## [1] "cor(startprice, D.nwrds.unq.log)=-0.1072"
## [1] "cor(startprice, D.terms.n.post.stem.log)=-0.1072"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.n.post.stem.log as highly correlated
## with D.nwrds.unq.log## [1] "cor(D.nwrds.unq.log, D.terms.n.post.stop.log)=0.9999"
## [1] "cor(startprice, D.nwrds.unq.log)=-0.1072"
## [1] "cor(startprice, D.terms.n.post.stop.log)=-0.1074"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nwrds.unq.log as highly correlated with
## D.terms.n.post.stop.log## [1] "cor(D.nchrs.log, D.nuppr.log)=0.9995"
## [1] "cor(startprice, D.nchrs.log)=-0.1160"
## [1] "cor(startprice, D.nuppr.log)=-0.1166"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nchrs.log as highly correlated with
## D.nuppr.log## [1] "cor(D.terms.n.post.stem, D.terms.n.post.stop)=0.9991"
## [1] "cor(startprice, D.terms.n.post.stem)=-0.0876"
## [1] "cor(startprice, D.terms.n.post.stop)=-0.0881"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.n.post.stem as highly correlated with
## D.terms.n.post.stop## [1] "cor(D.TfIdf.sum.post.stem, D.TfIdf.sum.post.stop)=0.9977"
## [1] "cor(startprice, D.TfIdf.sum.post.stem)=-0.1255"
## [1] "cor(startprice, D.TfIdf.sum.post.stop)=-0.1296"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.TfIdf.sum.post.stem as highly correlated with
## D.TfIdf.sum.post.stop## [1] "cor(D.nuppr.log, D.terms.n.post.stop.log)=0.9930"
## [1] "cor(startprice, D.nuppr.log)=-0.1166"
## [1] "cor(startprice, D.terms.n.post.stop.log)=-0.1074"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.n.post.stop.log as highly correlated
## with D.nuppr.log## [1] "cor(D.nuppr.log, D.nwrds.log)=0.9922"
## [1] "cor(startprice, D.nuppr.log)=-0.1166"
## [1] "cor(startprice, D.nwrds.log)=-0.1099"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nwrds.log as highly correlated with
## D.nuppr.log## [1] "cor(D.npnct24.log, D.nuppr.log)=0.9786"
## [1] "cor(startprice, D.npnct24.log)=-0.1250"
## [1] "cor(startprice, D.nuppr.log)=-0.1166"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nuppr.log as highly correlated with
## D.npnct24.log## [1] "cor(D.npnct24.log, D.ratio.nstopwrds.nwrds)=-0.9641"
## [1] "cor(startprice, D.npnct24.log)=-0.1250"
## [1] "cor(startprice, D.ratio.nstopwrds.nwrds)=0.1075"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.ratio.nstopwrds.nwrds as highly correlated
## with D.npnct24.log## [1] "cor(D.TfIdf.sum.post.stop, D.npnct24.log)=0.9640"
## [1] "cor(startprice, D.TfIdf.sum.post.stop)=-0.1296"
## [1] "cor(startprice, D.npnct24.log)=-0.1250"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct24.log as highly correlated with
## D.TfIdf.sum.post.stop## [1] "cor(D.npnct06.log, D.npnct16.log)=0.9556"
## [1] "cor(startprice, D.npnct06.log)=-0.0367"
## [1] "cor(startprice, D.npnct16.log)=-0.0338"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct16.log as highly correlated with
## D.npnct06.log## [1] "cor(D.nstopwrds.log, D.terms.n.post.stop)=0.8931"
## [1] "cor(startprice, D.nstopwrds.log)=-0.1049"
## [1] "cor(startprice, D.terms.n.post.stop)=-0.0881"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.n.post.stop as highly correlated with
## D.nstopwrds.log## [1] "cor(carrier.fctr, cellular.fctr)=0.8460"
## [1] "cor(startprice, carrier.fctr)=0.0826"
## [1] "cor(startprice, cellular.fctr)=0.0196"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified cellular.fctr as highly correlated with
## carrier.fctr## [1] "cor(D.TfIdf.sum.post.stop, D.nstopwrds.log)=0.8418"
## [1] "cor(startprice, D.TfIdf.sum.post.stop)=-0.1296"
## [1] "cor(startprice, D.nstopwrds.log)=-0.1049"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nstopwrds.log as highly correlated with
## D.TfIdf.sum.post.stop## id cor.y exclude.as.feat cor.y.abs
## 73 startprice.log 0.7052693713 1 0.7052693713
## 71 prdline.my.fctr 0.3382438492 0 0.3382438492
## 69 condition.fctr 0.3106762046 0 0.3106762046
## 68 color.fctr 0.1677888274 0 0.1677888274
## 19 D.TfIdf.sum.stem.stop.Ratio 0.1196299251 0 0.1196299251
## 56 D.ratio.nstopwrds.nwrds 0.1075484534 0 0.1075484534
## 64 UniqueID 0.1070161471 1 0.1070161471
## 70 idseq.my 0.1070161471 0 0.1070161471
## 66 carrier.fctr 0.0825701842 0 0.0825701842
## 30 D.npnct09.log 0.0625031263 0 0.0625031263
## 12 D.T.new 0.0423874258 1 0.0423874258
## 31 D.npnct10.log 0.0399079971 0 0.0399079971
## 63 D.terms.n.stem.stop.Ratio 0.0380363992 0 0.0380363992
## 4 D.P.air 0.0321511783 1 0.0321511783
## 49 D.npnct28.log 0.0283524789 0 0.0283524789
## 67 cellular.fctr 0.0196492177 0 0.0196492177
## 39 D.npnct18.log 0.0119378362 0 0.0119378362
## 35 D.npnct14.log 0.0107251700 0 0.0107251700
## 3 .rnorm 0.0076927240 0 0.0076927240
## 26 D.npnct05.log 0.0067080843 0 0.0067080843
## 29 D.npnct08.log -0.0004608043 0 0.0004608043
## 22 D.npnct01.log -0.0054440716 0 0.0054440716
## 6 D.P.mini -0.0068984971 1 0.0068984971
## 8 D.T.excel -0.0088931501 1 0.0088931501
## 21 D.ndgts.log -0.0108394471 0 0.0108394471
## 7 D.T.condit -0.0138032677 1 0.0138032677
## 33 D.npnct12.log -0.0186354165 0 0.0186354165
## 13 D.T.scratch -0.0282866598 1 0.0282866598
## 28 D.npnct07.log -0.0286463459 0 0.0286463459
## 37 D.npnct16.log -0.0338015713 0 0.0338015713
## 27 D.npnct06.log -0.0366796413 0 0.0366796413
## 10 D.T.great -0.0419289682 1 0.0419289682
## 36 D.npnct15.log -0.0420569179 0 0.0420569179
## 15 D.T.use -0.0450788187 1 0.0450788187
## 32 D.npnct11.log -0.0495381784 0 0.0495381784
## 24 D.npnct03.log -0.0516555162 0 0.0516555162
## 11 D.T.ipad -0.0550850230 1 0.0550850230
## 74 storage.fctr -0.0649793405 0 0.0649793405
## 9 D.T.good -0.0714102568 1 0.0714102568
## 16 D.T.work -0.0768613740 1 0.0768613740
## 34 D.npnct13.log -0.0787436811 0 0.0787436811
## 1 .clusterid -0.0801748511 1 0.0801748511
## 2 .clusterid.fctr -0.0801748511 0 0.0801748511
## 59 D.terms.n.post.stem -0.0876424112 0 0.0876424112
## 61 D.terms.n.post.stop -0.0881077263 0 0.0881077263
## 14 D.T.screen -0.0931731081 1 0.0931731081
## 57 D.ratio.sum.TfIdf.nwrds -0.1049203217 0 0.1049203217
## 52 D.nstopwrds.log -0.1049213237 0 0.1049213237
## 55 D.nwrds.unq.log -0.1071833048 0 0.1071833048
## 60 D.terms.n.post.stem.log -0.1071833048 0 0.1071833048
## 62 D.terms.n.post.stop.log -0.1073577343 0 0.1073577343
## 54 D.nwrds.log -0.1098932656 0 0.1098932656
## 20 D.nchrs.log -0.1159829723 0 0.1159829723
## 53 D.nuppr.log -0.1166425681 0 0.1166425681
## 45 D.npnct24.log -0.1250425485 0 0.1250425485
## 17 D.TfIdf.sum.post.stem -0.1254721539 0 0.1254721539
## 58 D.sum.TfIdf -0.1254721539 0 0.1254721539
## 18 D.TfIdf.sum.post.stop -0.1296482504 0 0.1296482504
## 65 biddable -0.4563275606 0 0.4563275606
## 72 sold -0.4569767211 1 0.4569767211
## 5 D.P.http NA 1 NA
## 23 D.npnct02.log NA 0 NA
## 25 D.npnct04.log NA 0 NA
## 38 D.npnct17.log NA 0 NA
## 40 D.npnct19.log NA 0 NA
## 41 D.npnct20.log NA 0 NA
## 42 D.npnct21.log NA 0 NA
## 43 D.npnct22.log NA 0 NA
## 44 D.npnct23.log NA 0 NA
## 46 D.npnct25.log NA 0 NA
## 47 D.npnct26.log NA 0 NA
## 48 D.npnct27.log NA 0 NA
## 50 D.npnct29.log NA 0 NA
## 51 D.npnct30.log NA 0 NA
## cor.high.X freqRatio percentUnique zeroVar nzv
## 73 <NA> 2.807692 30.17751479 FALSE FALSE
## 71 <NA> 1.135048 0.37654653 FALSE FALSE
## 69 <NA> 4.003460 0.32275417 FALSE FALSE
## 68 <NA> 1.574610 0.26896181 FALSE FALSE
## 19 <NA> 65.176471 32.92092523 FALSE FALSE
## 56 D.npnct24.log 13.048780 4.24959656 FALSE FALSE
## 64 <NA> 1.000000 100.00000000 FALSE FALSE
## 70 <NA> 1.000000 100.00000000 FALSE FALSE
## 66 <NA> 3.195965 0.37654653 FALSE FALSE
## 30 <NA> 308.333333 0.21516945 FALSE TRUE
## 12 <NA> 103.000000 0.86067778 FALSE TRUE
## 31 <NA> 308.666667 0.16137708 FALSE TRUE
## 63 <NA> 77.826087 0.48413125 FALSE TRUE
## 4 <NA> 122.866667 0.16137708 FALSE TRUE
## 49 <NA> 463.250000 0.16137708 FALSE TRUE
## 67 carrier.fctr 2.112381 0.16137708 FALSE FALSE
## 39 <NA> 1858.000000 0.10758472 FALSE TRUE
## 35 <NA> 35.333333 0.26896181 FALSE TRUE
## 3 <NA> 1.000000 100.00000000 FALSE FALSE
## 26 <NA> 40.311111 0.10758472 FALSE TRUE
## 29 <NA> 69.576923 0.21516945 FALSE TRUE
## 22 <NA> 52.970588 0.32275417 FALSE TRUE
## 6 <NA> 91.900000 0.16137708 FALSE TRUE
## 8 <NA> 128.285714 0.75309306 FALSE TRUE
## 21 <NA> 27.031746 0.69930070 FALSE TRUE
## 7 <NA> 22.984848 0.91447015 FALSE TRUE
## 33 <NA> 26.818182 0.21516945 FALSE TRUE
## 13 <NA> 41.400000 0.86067778 FALSE TRUE
## 28 <NA> 1858.000000 0.10758472 FALSE TRUE
## 37 D.npnct06.log 31.245614 0.16137708 FALSE TRUE
## 27 <NA> 33.735849 0.16137708 FALSE TRUE
## 10 <NA> 118.400000 0.80688542 FALSE TRUE
## 36 <NA> 153.416667 0.16137708 FALSE TRUE
## 15 <NA> 44.675676 0.91447015 FALSE TRUE
## 32 <NA> 9.374269 0.37654653 FALSE FALSE
## 24 <NA> 83.227273 0.16137708 FALSE TRUE
## 11 <NA> 49.823529 0.80688542 FALSE TRUE
## 74 <NA> 2.733138 0.26896181 FALSE FALSE
## 9 <NA> 45.315789 0.86067778 FALSE TRUE
## 16 <NA> 59.241379 0.69930070 FALSE TRUE
## 34 <NA> 5.203065 0.32275417 FALSE FALSE
## 1 <NA> 3.987013 0.26896181 FALSE FALSE
## 2 <NA> 3.987013 0.26896181 FALSE FALSE
## 59 D.terms.n.post.stop 7.595745 0.80688542 FALSE FALSE
## 61 D.nstopwrds.log 8.052632 0.80688542 FALSE FALSE
## 14 <NA> 53.343750 0.80688542 FALSE TRUE
## 57 <NA> 63.000000 34.85745024 FALSE FALSE
## 52 D.TfIdf.sum.post.stop 13.916667 0.80688542 FALSE FALSE
## 55 D.terms.n.post.stop.log 7.595745 0.80688542 FALSE FALSE
## 60 D.nwrds.unq.log 7.595745 0.80688542 FALSE FALSE
## 62 D.nuppr.log 8.052632 0.80688542 FALSE FALSE
## 54 D.nuppr.log 12.891566 1.29101668 FALSE FALSE
## 20 D.nuppr.log 15.970149 5.70199032 FALSE FALSE
## 53 D.npnct24.log 18.807018 4.41097364 FALSE FALSE
## 45 D.TfIdf.sum.post.stop 1.356147 0.10758472 FALSE FALSE
## 17 D.TfIdf.sum.post.stop 63.000000 34.31952663 FALSE FALSE
## 58 D.TfIdf.sum.post.stem 63.000000 34.31952663 FALSE FALSE
## 18 <NA> 63.000000 34.42711135 FALSE FALSE
## 65 <NA> 1.221027 0.10758472 FALSE FALSE
## 72 <NA> 1.161628 0.10758472 FALSE FALSE
## 5 <NA> 0.000000 0.05379236 TRUE TRUE
## 23 <NA> 0.000000 0.05379236 TRUE TRUE
## 25 <NA> 0.000000 0.05379236 TRUE TRUE
## 38 <NA> 0.000000 0.05379236 TRUE TRUE
## 40 <NA> 0.000000 0.05379236 TRUE TRUE
## 41 <NA> 0.000000 0.05379236 TRUE TRUE
## 42 <NA> 0.000000 0.05379236 TRUE TRUE
## 43 <NA> 0.000000 0.05379236 TRUE TRUE
## 44 <NA> 0.000000 0.05379236 TRUE TRUE
## 46 <NA> 0.000000 0.05379236 TRUE TRUE
## 47 <NA> 0.000000 0.05379236 TRUE TRUE
## 48 <NA> 0.000000 0.05379236 TRUE TRUE
## 50 <NA> 0.000000 0.05379236 TRUE TRUE
## 51 <NA> 0.000000 0.05379236 TRUE TRUE
## myNearZV is.cor.y.abs.low
## 73 FALSE FALSE
## 71 FALSE FALSE
## 69 FALSE FALSE
## 68 FALSE FALSE
## 19 FALSE FALSE
## 56 FALSE FALSE
## 64 FALSE FALSE
## 70 FALSE FALSE
## 66 FALSE FALSE
## 30 FALSE FALSE
## 12 FALSE FALSE
## 31 FALSE FALSE
## 63 FALSE FALSE
## 4 FALSE FALSE
## 49 FALSE FALSE
## 67 FALSE FALSE
## 39 TRUE FALSE
## 35 FALSE FALSE
## 3 FALSE FALSE
## 26 FALSE TRUE
## 29 FALSE TRUE
## 22 FALSE TRUE
## 6 FALSE TRUE
## 8 FALSE FALSE
## 21 FALSE FALSE
## 7 FALSE FALSE
## 33 FALSE FALSE
## 13 FALSE FALSE
## 28 TRUE FALSE
## 37 FALSE FALSE
## 27 FALSE FALSE
## 10 FALSE FALSE
## 36 FALSE FALSE
## 15 FALSE FALSE
## 32 FALSE FALSE
## 24 FALSE FALSE
## 11 FALSE FALSE
## 74 FALSE FALSE
## 9 FALSE FALSE
## 16 FALSE FALSE
## 34 FALSE FALSE
## 1 FALSE FALSE
## 2 FALSE FALSE
## 59 FALSE FALSE
## 61 FALSE FALSE
## 14 FALSE FALSE
## 57 FALSE FALSE
## 52 FALSE FALSE
## 55 FALSE FALSE
## 60 FALSE FALSE
## 62 FALSE FALSE
## 54 FALSE FALSE
## 20 FALSE FALSE
## 53 FALSE FALSE
## 45 FALSE FALSE
## 17 FALSE FALSE
## 58 FALSE FALSE
## 18 FALSE FALSE
## 65 FALSE FALSE
## 72 FALSE FALSE
## 5 TRUE NA
## 23 TRUE NA
## 25 TRUE NA
## 38 TRUE NA
## 40 TRUE NA
## 41 TRUE NA
## 42 TRUE NA
## 43 TRUE NA
## 44 TRUE NA
## 46 TRUE NA
## 47 TRUE NA
## 48 TRUE NA
## 50 TRUE NA
## 51 TRUE NA#subset(glb_feats_df, id %in% c("A.nuppr.log", "S.nuppr.log"))
print(myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
colorcol_name="myNearZV", jitter=TRUE) +
geom_point(aes(shape=nzv)) + xlim(-5, 25))## Warning in myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "myNearZV", : converting myNearZV to class:factor## Warning: Removed 9 rows containing missing values (geom_point).## Warning: Removed 9 rows containing missing values (geom_point).## Warning: Removed 9 rows containing missing values (geom_point).
print(subset(glb_feats_df, myNearZV))## id cor.y exclude.as.feat cor.y.abs cor.high.X
## 39 D.npnct18.log 0.01193784 0 0.01193784 <NA>
## 28 D.npnct07.log -0.02864635 0 0.02864635 <NA>
## 5 D.P.http NA 1 NA <NA>
## 23 D.npnct02.log NA 0 NA <NA>
## 25 D.npnct04.log NA 0 NA <NA>
## 38 D.npnct17.log NA 0 NA <NA>
## 40 D.npnct19.log NA 0 NA <NA>
## 41 D.npnct20.log NA 0 NA <NA>
## 42 D.npnct21.log NA 0 NA <NA>
## 43 D.npnct22.log NA 0 NA <NA>
## 44 D.npnct23.log NA 0 NA <NA>
## 46 D.npnct25.log NA 0 NA <NA>
## 47 D.npnct26.log NA 0 NA <NA>
## 48 D.npnct27.log NA 0 NA <NA>
## 50 D.npnct29.log NA 0 NA <NA>
## 51 D.npnct30.log NA 0 NA <NA>
## freqRatio percentUnique zeroVar nzv myNearZV is.cor.y.abs.low
## 39 1858 0.10758472 FALSE TRUE TRUE FALSE
## 28 1858 0.10758472 FALSE TRUE TRUE FALSE
## 5 0 0.05379236 TRUE TRUE TRUE NA
## 23 0 0.05379236 TRUE TRUE TRUE NA
## 25 0 0.05379236 TRUE TRUE TRUE NA
## 38 0 0.05379236 TRUE TRUE TRUE NA
## 40 0 0.05379236 TRUE TRUE TRUE NA
## 41 0 0.05379236 TRUE TRUE TRUE NA
## 42 0 0.05379236 TRUE TRUE TRUE NA
## 43 0 0.05379236 TRUE TRUE TRUE NA
## 44 0 0.05379236 TRUE TRUE TRUE NA
## 46 0 0.05379236 TRUE TRUE TRUE NA
## 47 0 0.05379236 TRUE TRUE TRUE NA
## 48 0 0.05379236 TRUE TRUE TRUE NA
## 50 0 0.05379236 TRUE TRUE TRUE NA
## 51 0 0.05379236 TRUE TRUE TRUE NAglb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
subset(glb_feats_df, myNearZV)$id)]
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
if (!is.null(glb_interaction_only_features))
glb_feats_df[glb_feats_df$id %in% glb_interaction_only_features, "interaction.feat"] <-
names(glb_interaction_only_features) else
glb_feats_df$interaction.feat <- NA
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## sold
## 798
## [1] "numeric data w/ 0s in : "
## biddable sold startprice.log
## 1444 999 31
## cellular.fctr D.terms.n.post.stop D.terms.n.post.stop.log
## 1597 1521 1521
## D.TfIdf.sum.post.stop D.terms.n.post.stem D.terms.n.post.stem.log
## 1521 1521 1521
## D.TfIdf.sum.post.stem D.T.condit D.T.use
## 1521 2161 2366
## D.T.scratch D.T.new D.T.good
## 2371 2501 2460
## D.T.ipad D.T.screen D.T.great
## 2425 2444 2532
## D.T.work D.T.excel D.nwrds.log
## 2459 2557 1520
## D.nwrds.unq.log D.sum.TfIdf D.ratio.sum.TfIdf.nwrds
## 1521 1521 1521
## D.nchrs.log D.nuppr.log D.ndgts.log
## 1520 1522 2426
## D.npnct01.log D.npnct03.log D.npnct05.log
## 2579 2614 2592
## D.npnct06.log D.npnct08.log D.npnct09.log
## 2554 2581 2641
## D.npnct10.log D.npnct11.log D.npnct12.log
## 2648 2301 2537
## D.npnct13.log D.npnct14.log D.npnct15.log
## 1932 2582 2637
## D.npnct16.log D.npnct24.log D.npnct28.log
## 2546 1520 2649
## D.nstopwrds.log D.P.mini D.P.air
## 1663 2623 2637
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid prdline.my descr.my
## 0 NA 0 1520# glb_allobs_df %>% filter(is.na(Married.fctr)) %>% tbl_df()
# glb_allobs_df %>% count(Married.fctr)
# levels(glb_allobs_df$Married.fctr)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "partition.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 8 select.features 5 0 93.670 97.445 3.775
## 9 partition.data.training 6 0 97.445 NA NAStep 6.0: partition data training
if (all(is.na(glb_newobs_df[, glb_rsp_var]))) {
set.seed(glb_split_sample.seed)
OOB_size <- nrow(glb_newobs_df) * 1.1
if (is.null(glb_category_var)) {
require(caTools)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=OOB_size / nrow(glb_trnobs_df))
glb_OOBobs_df <- glb_trnobs_df[split ,]
glb_fitobs_df <- glb_trnobs_df[!split, ]
} else {
sample_vars <- c(glb_rsp_var_raw, glb_category_var)
rspvar_freq_df <- orderBy(reformulate(glb_rsp_var_raw),
mycreate_sqlxtab_df(glb_trnobs_df, glb_rsp_var_raw))
OOB_rspvar_size <- 1.0 * OOB_size * rspvar_freq_df$.n / sum(rspvar_freq_df$.n)
newobs_freq_df <- orderBy(reformulate(glb_category_var),
mycreate_sqlxtab_df(glb_newobs_df, glb_category_var))
trnobs_freq_df <- orderBy(reformulate(glb_category_var),
mycreate_sqlxtab_df(glb_trnobs_df, glb_category_var))
allobs_freq_df <- merge(newobs_freq_df, trnobs_freq_df, by=glb_category_var,
all=TRUE, sort=TRUE, suffixes=c(".Tst", ".Train"))
allobs_freq_df[is.na(allobs_freq_df)] <- 0
OOB_strata_size <- ceiling(
as.vector(matrix(allobs_freq_df$.n.Tst * 1.0 / sum(allobs_freq_df$.n.Tst)) %*%
matrix(OOB_rspvar_size, nrow=1)))
OOB_strata_size[OOB_strata_size == 0] <- 1
OOB_strata_df <- expand.grid(glb_rsp_var_raw=rspvar_freq_df[, glb_rsp_var_raw],
glb_category_var=allobs_freq_df[, glb_category_var])
names(OOB_strata_df) <- sample_vars
OOB_strata_df <- orderBy(reformulate(sample_vars), OOB_strata_df)
trnobs_univ_df <- orderBy(reformulate(sample_vars),
mycreate_sqlxtab_df(glb_trnobs_df, sample_vars))
trnobs_univ_df <- merge(trnobs_univ_df, OOB_strata_df, all=TRUE)
tmp_trnobs_df <- orderBy(reformulate(c(glb_rsp_var_raw, glb_category_var)),
glb_trnobs_df)
require(sampling)
split_strata <- strata(tmp_trnobs_df,
stratanames=c(glb_rsp_var_raw, glb_category_var),
size=OOB_strata_size[!is.na(trnobs_univ_df$.n)],
method="srswor")
glb_OOBobs_df <- getdata(tmp_trnobs_df, split_strata)[, names(glb_trnobs_df)]
glb_fitobs_df <- glb_trnobs_df[!glb_trnobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ]
}
} else {
print(sprintf("Newdata contains non-NA data for %s; setting OOB to Newdata",
glb_rsp_var))
glb_fitobs_df <- glb_trnobs_df; glb_OOBobs_df <- glb_newobs_df
}## [1] "Newdata contains non-NA data for startprice; setting OOB to Newdata"if (!is.null(glb_max_fitobs) && (nrow(glb_fitobs_df) > glb_max_fitobs)) {
warning("glb_fitobs_df restricted to glb_max_fitobs: ",
format(glb_max_fitobs, big.mark=","))
org_fitobs_df <- glb_fitobs_df
glb_fitobs_df <-
org_fitobs_df[split <- sample.split(org_fitobs_df[, glb_rsp_var_raw],
SplitRatio=glb_max_fitobs), ]
org_fitobs_df <- NULL
}
glb_allobs_df$.lcn <- ""
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_fitobs_df[, glb_id_var], ".lcn"] <- "Fit"
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ".lcn"] <- "OOB"
dsp_class_dstrb <- function(obs_df, location_var, partition_var) {
xtab_df <- mycreate_xtab_df(obs_df, c(location_var, partition_var))
rownames(xtab_df) <- xtab_df[, location_var]
xtab_df <- xtab_df[, -grepl(location_var, names(xtab_df))]
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Ensure proper splits by glb_rsp_var_raw & user-specified feature for OOB vs. new
if (!is.null(glb_category_var)) {
if (glb_is_classification)
dsp_class_dstrb(glb_allobs_df, ".lcn", glb_rsp_var_raw)
newobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .src == "Test"),
glb_category_var)
OOBobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .lcn == "OOB"),
glb_category_var)
glb_ctgry_df <- merge(newobs_ctgry_df, OOBobs_ctgry_df, by=glb_category_var
, all=TRUE, suffixes=c(".Tst", ".OOB"))
glb_ctgry_df$.freqRatio.Tst <- glb_ctgry_df$.n.Tst / sum(glb_ctgry_df$.n.Tst, na.rm=TRUE)
glb_ctgry_df$.freqRatio.OOB <- glb_ctgry_df$.n.OOB / sum(glb_ctgry_df$.n.OOB, na.rm=TRUE)
print(orderBy(~-.freqRatio.Tst-.freqRatio.OOB, glb_ctgry_df))
}## prdline.my .n.Tst .n.OOB .freqRatio.Tst .freqRatio.OOB
## 3 iPad 2 154 154 0.1929825 0.1929825
## 5 iPadAir 137 137 0.1716792 0.1716792
## 4 iPad 3+ 123 123 0.1541353 0.1541353
## 6 iPadmini 114 114 0.1428571 0.1428571
## 7 iPadmini 2+ 94 94 0.1177945 0.1177945
## 2 iPad 1 89 89 0.1115288 0.1115288
## 1 Unknown 87 87 0.1090226 0.1090226# Run this line by line
print("glb_feats_df:"); print(dim(glb_feats_df))## [1] "glb_feats_df:"## [1] 74 12sav_feats_df <- glb_feats_df
glb_feats_df <- sav_feats_df
glb_feats_df[, "rsp_var_raw"] <- FALSE
glb_feats_df[glb_feats_df$id == glb_rsp_var_raw, "rsp_var_raw"] <- TRUE
glb_feats_df$exclude.as.feat <- (glb_feats_df$exclude.as.feat == 1)
if (!is.null(glb_id_var) && glb_id_var != ".rownames")
glb_feats_df[glb_feats_df$id %in% glb_id_var, "id_var"] <- TRUE
add_feats_df <- data.frame(id=glb_rsp_var, exclude.as.feat=TRUE, rsp_var=TRUE)
row.names(add_feats_df) <- add_feats_df$id; print(add_feats_df)## id exclude.as.feat rsp_var
## startprice startprice TRUE TRUEglb_feats_df <- myrbind_df(glb_feats_df, add_feats_df)
if (glb_id_var != ".rownames")
print(subset(glb_feats_df, rsp_var_raw | rsp_var | id_var)) else
print(subset(glb_feats_df, rsp_var_raw | rsp_var)) ## id cor.y exclude.as.feat cor.y.abs cor.high.X
## 64 UniqueID 0.1070161 TRUE 0.1070161 <NA>
## startprice startprice NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv myNearZV is.cor.y.abs.low
## 64 1 100 FALSE FALSE FALSE FALSE
## startprice NA NA NA NA NA NA
## interaction.feat rsp_var_raw id_var rsp_var
## 64 <NA> FALSE TRUE NA
## startprice <NA> NA NA TRUEprint("glb_feats_df vs. glb_allobs_df: "); ## [1] "glb_feats_df vs. glb_allobs_df: "print(setdiff(glb_feats_df$id, names(glb_allobs_df)))## [1] "D.npnct18.log" "D.npnct07.log" "D.P.http" "D.npnct02.log"
## [5] "D.npnct04.log" "D.npnct17.log" "D.npnct19.log" "D.npnct20.log"
## [9] "D.npnct21.log" "D.npnct22.log" "D.npnct23.log" "D.npnct25.log"
## [13] "D.npnct26.log" "D.npnct27.log" "D.npnct29.log" "D.npnct30.log"print("glb_allobs_df vs. glb_feats_df: "); ## [1] "glb_allobs_df vs. glb_feats_df: "# Ensure these are only chr vars
print(setdiff(setdiff(names(glb_allobs_df), glb_feats_df$id),
myfind_chr_cols_df(glb_allobs_df)))## character(0)#print(setdiff(setdiff(names(glb_allobs_df), glb_exclude_vars_as_features),
# glb_feats_df$id))
print("glb_allobs_df: "); print(dim(glb_allobs_df))## [1] "glb_allobs_df: "## [1] 2657 71print("glb_trnobs_df: "); print(dim(glb_trnobs_df))## [1] "glb_trnobs_df: "## [1] 1859 70print("glb_fitobs_df: "); print(dim(glb_fitobs_df))## [1] "glb_fitobs_df: "## [1] 1859 70print("glb_OOBobs_df: "); print(dim(glb_OOBobs_df))## [1] "glb_OOBobs_df: "## [1] 798 70print("glb_newobs_df: "); print(dim(glb_newobs_df))## [1] "glb_newobs_df: "## [1] 798 70# # Does not handle NULL or length(glb_id_var) > 1
# glb_allobs_df$.src.trn <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_trnobs_df[, glb_id_var],
# ".src.trn"] <- 1
# glb_allobs_df$.src.fit <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_fitobs_df[, glb_id_var],
# ".src.fit"] <- 1
# glb_allobs_df$.src.OOB <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_OOBobs_df[, glb_id_var],
# ".src.OOB"] <- 1
# glb_allobs_df$.src.new <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_newobs_df[, glb_id_var],
# ".src.new"] <- 1
# #print(unique(glb_allobs_df[, ".src.trn"]))
# write_cols <- c(glb_feats_df$id,
# ".src.trn", ".src.fit", ".src.OOB", ".src.new")
# glb_allobs_df <- glb_allobs_df[, write_cols]
#
# tmp_feats_df <- glb_feats_df
# tmp_entity_df <- glb_allobs_df
if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
file=paste0(glb_out_pfx, "blddfs_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
# if (!all.equal(tmp_feats_df, glb_feats_df))
# stop("glb_feats_df r/w not working")
# if (!all.equal(tmp_entity_df, glb_allobs_df))
# stop("glb_allobs_df r/w not working")
rm(split)## Warning in rm(split): object 'split' not foundglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 partition.data.training 6 0 97.445 97.894 0.449
## 10 fit.models 7 0 97.894 NA NAStep 7.0: fit models
# load(paste0(glb_out_pfx, "dsk.RData"))
# keep_cols <- setdiff(names(glb_allobs_df),
# grep("^.src", names(glb_allobs_df), value=TRUE))
# glb_trnobs_df <- glb_allobs_df[glb_allobs_df$.src.trn == 1, keep_cols]
# glb_fitobs_df <- glb_allobs_df[glb_allobs_df$.src.fit == 1, keep_cols]
# glb_OOBobs_df <- glb_allobs_df[glb_allobs_df$.src.OOB == 1, keep_cols]
# glb_newobs_df <- glb_allobs_df[glb_allobs_df$.src.new == 1, keep_cols]
#
# glb_models_lst <- list(); glb_models_df <- data.frame()
#
if (glb_is_classification && glb_is_binomial &&
(length(unique(glb_fitobs_df[, glb_rsp_var])) < 2))
stop("glb_fitobs_df$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glb_fitobs_df[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
# while(length(max_cor_y_x_vars) < 2) {
# max_cor_y_x_vars <- c(max_cor_y_x_vars, orderBy(~ -cor.y.abs,
# subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low))[3, "id"])
# }
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Baseline
if (!is.null(glb_Baseline_mdl_var))
ret_lst <- myfit_mdl(model_id="Baseline",
model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(model_id="MFO",
model_method=ifelse(glb_is_regression, "lm", "myMFO_classfr"),
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: MFO.lm"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -214.81 -129.29 -31.79 90.80 787.85
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 211.342 3.953 53.463 <2e-16 ***
## .rnorm 1.331 4.016 0.332 0.74
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 170.4 on 1857 degrees of freedom
## Multiple R-squared: 5.918e-05, Adjusted R-squared: -0.0004793
## F-statistic: 0.1099 on 1 and 1857 DF, p-value: 0.7403
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO.lm lm .rnorm 0 0.486
## min.elapsedtime.final max.R.sq.fit min.RMSE.fit max.R.sq.OOB
## 1 0.004 5.9178e-05 170.3485 -0.0009216371
## min.RMSE.OOB max.Adj.R.sq.fit
## 1 173.3545 -0.0004792931if (glb_is_classification)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
ret_lst <- myfit_mdl(model_id="Random", model_method="myrandom_classfr",
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Any models that have tuning parameters has "better" results with cross-validation
# (except rf) & "different" results for different outcome metrics
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.cv.0.rpart"
## [1] " indep_vars: biddable, prdline.my.fctr"## Loading required package: rpart## Fitting cp = 0.208 on full training set## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 1859
##
## CP nsplit rel error
## 1 0.2082348 0 1
##
## Node number 1: 1859 observations
## mean=211.3404, MSE=29020.33
##
## n= 1859
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 1859 53948800 211.3404 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats
## 1 Max.cor.Y.cv.0.rpart rpart biddable, prdline.my.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.635 0.015
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB
## 1 0 170.3535 0 173.2747ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0.cp.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=0,
tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))## [1] "fitting model: Max.cor.Y.cv.0.cp.0.rpart"
## [1] " indep_vars: biddable, prdline.my.fctr"
## Fitting cp = 0 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 1859
##
## CP nsplit rel error
## 1 0.2082348426 0 1.0000000
## 2 0.1508301805 1 0.7917652
## 3 0.0520382853 2 0.6409350
## 4 0.0343035868 3 0.5888967
## 5 0.0206652917 4 0.5545931
## 6 0.0192102188 5 0.5339278
## 7 0.0099497963 6 0.5147176
## 8 0.0034593567 7 0.5047678
## 9 0.0034288114 8 0.5013084
## 10 0.0026020040 9 0.4978796
## 11 0.0007005834 10 0.4952776
## 12 0.0004961822 11 0.4945770
## 13 0.0000259760 12 0.4940809
## 14 0.0000000000 13 0.4940549
##
## Variable importance
## biddable prdline.my.fctriPadAir
## 41 36
## prdline.my.fctriPadmini 2+ prdline.my.fctriPad 1
## 14 5
## prdline.my.fctriPad 3+ prdline.my.fctriPad 2
## 2 1
##
## Node number 1: 1859 observations, complexity param=0.2082348
## mean=211.3404, MSE=29020.33
## left son=2 (837 obs) right son=3 (1022 obs)
## Primary splits:
## biddable < 0.5 to the right, improve=0.20823480, (0 missing)
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.19258840, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.07643644, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.04433587, (0 missing)
## prdline.my.fctriPadmini 2+ < 0.5 to the left, improve=0.04370009, (0 missing)
## Surrogate splits:
## prdline.my.fctriPad 1 < 0.5 to the right, agree=0.556, adj=0.013, (0 split)
##
## Node number 2: 837 observations, complexity param=0.03430359
## mean=125.4409, MSE=18411.58
## left son=4 (696 obs) right son=5 (141 obs)
## Primary splits:
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.120089500, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.044170840, (0 missing)
## prdline.my.fctriPadmini 2+ < 0.5 to the left, improve=0.042851130, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.030950400, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the right, improve=0.008936269, (0 missing)
##
## Node number 3: 1022 observations, complexity param=0.1508302
## mean=281.6905, MSE=26716.52
## left son=6 (810 obs) right son=7 (212 obs)
## Primary splits:
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.29801570, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.11899300, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.06559892, (0 missing)
## prdline.my.fctriPadmini 2+ < 0.5 to the left, improve=0.04364408, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the right, improve=0.01406053, (0 missing)
##
## Node number 4: 696 observations, complexity param=0.01921022
## mean=104.2766, MSE=12474.79
## left son=8 (619 obs) right son=9 (77 obs)
## Primary splits:
## prdline.my.fctriPadmini 2+ < 0.5 to the left, improve=0.119363500, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.039662430, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.020797640, (0 missing)
## prdline.my.fctriPad 3+ < 0.5 to the left, improve=0.004664370, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the right, improve=0.001232649, (0 missing)
##
## Node number 5: 141 observations
## mean=229.9112, MSE=34591.47
##
## Node number 6: 810 observations, complexity param=0.05203829
## mean=236.0411, MSE=17881.31
## left son=12 (682 obs) right son=13 (128 obs)
## Primary splits:
## prdline.my.fctriPadmini 2+ < 0.5 to the left, improve=1.938297e-01, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=1.210065e-01, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=3.932612e-02, (0 missing)
## prdline.my.fctriPad 3+ < 0.5 to the left, improve=2.276499e-02, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the right, improve=6.924801e-05, (0 missing)
##
## Node number 7: 212 observations
## mean=456.1057, MSE=22091.13
##
## Node number 8: 619 observations, complexity param=0.003428811
## mean=90.6668, MSE=8821.231
## left son=16 (118 obs) right son=17 (501 obs)
## Primary splits:
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.033877050, (0 missing)
## prdline.my.fctriPad 3+ < 0.5 to the left, improve=0.027752350, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.011327070, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the left, improve=0.001019907, (0 missing)
##
## Node number 9: 77 observations
## mean=213.6855, MSE=28386.29
##
## Node number 12: 682 observations, complexity param=0.02066529
## mean=210.5363, MSE=14338.51
## left son=24 (107 obs) right son=25 (575 obs)
## Primary splits:
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.114007900, (0 missing)
## prdline.my.fctriPad 3+ < 0.5 to the left, improve=0.096469760, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.018074460, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the left, improve=0.009874778, (0 missing)
##
## Node number 13: 128 observations
## mean=371.9341, MSE=14824.98
##
## Node number 16: 118 observations
## mean=55.04669, MSE=3858.357
##
## Node number 17: 501 observations, complexity param=0.002602004
## mean=99.05637, MSE=9620.909
## left son=34 (140 obs) right son=35 (361 obs)
## Primary splits:
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.0291229900, (0 missing)
## prdline.my.fctriPad 3+ < 0.5 to the left, improve=0.0169207700, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the right, improve=0.0002901118, (0 missing)
##
## Node number 24: 107 observations
## mean=116.81, MSE=3071.362
##
## Node number 25: 575 observations, complexity param=0.009949796
## mean=227.9775, MSE=14496.28
## left son=50 (406 obs) right son=51 (169 obs)
## Primary splits:
## prdline.my.fctriPad 3+ < 0.5 to the left, improve=0.0643978900, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.0551350100, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the left, improve=0.0007316158, (0 missing)
##
## Node number 34: 140 observations
## mean=72.17721, MSE=4107.326
##
## Node number 35: 361 observations, complexity param=0.0007005834
## mean=109.4804, MSE=11370.29
## left son=70 (136 obs) right son=71 (225 obs)
## Primary splits:
## prdline.my.fctriPadmini < 0.5 to the right, improve=0.009207948, (0 missing)
## prdline.my.fctriPad 3+ < 0.5 to the left, improve=0.005547535, (0 missing)
## Surrogate splits:
## prdline.my.fctriPad 3+ < 0.5 to the left, agree=0.77, adj=0.39, (0 split)
##
## Node number 50: 406 observations, complexity param=0.003459357
## mean=208.2649, MSE=14138.36
## left son=100 (148 obs) right son=101 (258 obs)
## Primary splits:
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.03251263, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the left, improve=0.02475849, (0 missing)
## Surrogate splits:
## prdline.my.fctriPadmini < 0.5 to the left, agree=0.717, adj=0.223, (0 split)
##
## Node number 51: 169 observations
## mean=275.3345, MSE=12179.92
##
## Node number 70: 136 observations
## mean=96.31941, MSE=7136.302
##
## Node number 71: 225 observations, complexity param=2.5976e-05
## mean=117.4355, MSE=13761.52
## left son=142 (83 obs) right son=143 (142 obs)
## Primary splits:
## prdline.my.fctriPad 3+ < 0.5 to the left, improve=0.0004525902, (0 missing)
##
## Node number 100: 148 observations
## mean=179.9572, MSE=5402.821
##
## Node number 101: 258 observations, complexity param=0.0004961822
## mean=224.5034, MSE=18426.07
## left son=202 (115 obs) right son=203 (143 obs)
## Primary splits:
## prdline.my.fctriPadmini < 0.5 to the left, improve=0.005630804, (0 missing)
##
## Node number 142: 83 observations
## mean=114.1712, MSE=19470.43
##
## Node number 143: 142 observations
## mean=119.3435, MSE=10414.75
##
## Node number 202: 115 observations
## mean=213.145, MSE=23705.89
##
## Node number 203: 143 observations
## mean=233.6379, MSE=13992.88
##
## n= 1859
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 1859 53948800.0 211.34040
## 2) biddable>=0.5 837 15410490.0 125.44090
## 4) prdline.my.fctriPadAir< 0.5 696 8682454.0 104.27660
## 8) prdline.my.fctriPadmini 2+< 0.5 619 5460342.0 90.66680
## 16) prdline.my.fctriPad 1>=0.5 118 455286.1 55.04669 *
## 17) prdline.my.fctriPad 1< 0.5 501 4820075.0 99.05637
## 34) prdline.my.fctriPad 2>=0.5 140 575025.6 72.17721 *
## 35) prdline.my.fctriPad 2< 0.5 361 4104675.0 109.48040
## 70) prdline.my.fctriPadmini>=0.5 136 970537.1 96.31941 *
## 71) prdline.my.fctriPadmini< 0.5 225 3096342.0 117.43550
## 142) prdline.my.fctriPad 3+< 0.5 83 1616045.0 114.17120 *
## 143) prdline.my.fctriPad 3+>=0.5 142 1478895.0 119.34350 *
## 9) prdline.my.fctriPadmini 2+>=0.5 77 2185744.0 213.68550 *
## 5) prdline.my.fctriPadAir>=0.5 141 4877398.0 229.91120 *
## 3) biddable< 0.5 1022 27304290.0 281.69050
## 6) prdline.my.fctriPadAir< 0.5 810 14483860.0 236.04110
## 12) prdline.my.fctriPadmini 2+< 0.5 682 9778862.0 210.53630
## 24) prdline.my.fctriPad 1>=0.5 107 328635.7 116.81000 *
## 25) prdline.my.fctriPad 1< 0.5 575 8335358.0 227.97750
## 50) prdline.my.fctriPad 3+< 0.5 406 5740173.0 208.26490
## 100) prdline.my.fctriPad 2>=0.5 148 799617.6 179.95720 *
## 101) prdline.my.fctriPad 2< 0.5 258 4753927.0 224.50340
## 202) prdline.my.fctriPadmini< 0.5 115 2726177.0 213.14500 *
## 203) prdline.my.fctriPadmini>=0.5 143 2000982.0 233.63790 *
## 51) prdline.my.fctriPad 3+>=0.5 169 2058406.0 275.33450 *
## 13) prdline.my.fctriPadmini 2+>=0.5 128 1897598.0 371.93410 *
## 7) prdline.my.fctriPadAir>=0.5 212 4683319.0 456.10570 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats
## 1 Max.cor.Y.cv.0.cp.0.rpart rpart biddable, prdline.my.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.478 0.011
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.5059451 119.7399 0.4508174 128.4084if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.rpart"
## [1] " indep_vars: biddable, prdline.my.fctr"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.052 on full training set## Warning in myfit_mdl(model_id = "Max.cor.Y", model_method = "rpart",
## model_type = glb_model_type, : model's bestTune found at an extreme of
## tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 1859
##
## CP nsplit rel error
## 1 0.20823484 0 1.0000000
## 2 0.15083018 1 0.7917652
## 3 0.05203829 2 0.6409350
##
## Variable importance
## biddable prdline.my.fctriPadAir prdline.my.fctriPad 1
## 58 42 1
##
## Node number 1: 1859 observations, complexity param=0.2082348
## mean=211.3404, MSE=29020.33
## left son=2 (837 obs) right son=3 (1022 obs)
## Primary splits:
## biddable < 0.5 to the right, improve=0.20823480, (0 missing)
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.19258840, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.07643644, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.04433587, (0 missing)
## prdline.my.fctriPadmini 2+ < 0.5 to the left, improve=0.04370009, (0 missing)
## Surrogate splits:
## prdline.my.fctriPad 1 < 0.5 to the right, agree=0.556, adj=0.013, (0 split)
##
## Node number 2: 837 observations
## mean=125.4409, MSE=18411.58
##
## Node number 3: 1022 observations, complexity param=0.1508302
## mean=281.6905, MSE=26716.52
## left son=6 (810 obs) right son=7 (212 obs)
## Primary splits:
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.29801570, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.11899300, (0 missing)
## prdline.my.fctriPad 2 < 0.5 to the right, improve=0.06559892, (0 missing)
## prdline.my.fctriPadmini 2+ < 0.5 to the left, improve=0.04364408, (0 missing)
## prdline.my.fctriPadmini < 0.5 to the right, improve=0.01406053, (0 missing)
##
## Node number 6: 810 observations
## mean=236.0411, MSE=17881.31
##
## Node number 7: 212 observations
## mean=456.1057, MSE=22091.13
##
## n= 1859
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 1859 53948800 211.3404
## 2) biddable>=0.5 837 15410490 125.4409 *
## 3) biddable< 0.5 1022 27304290 281.6905
## 6) prdline.my.fctriPadAir< 0.5 810 14483860 236.0411 *
## 7) prdline.my.fctriPadAir>=0.5 212 4683319 456.1057 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.rpart rpart biddable, prdline.my.fctr 3
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 1.025 0.015 0.359065
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit min.RMSESD.fit
## 1 134.8184 0.3209143 142.7899 0.3757515 2.62909
## max.RsquaredSD.fit
## 1 0.02583973# Used to compare vs. Interactions.High.cor.Y and/or Max.cor.Y.TmSrs
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.lm"
## [1] " indep_vars: biddable, prdline.my.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -363.15 -68.58 -16.28 55.66 763.48
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 230.837 9.038 25.539 < 2e-16 ***
## biddable -141.178 5.741 -24.589 < 2e-16 ***
## `prdline.my.fctriPad 1` -72.378 11.961 -6.051 1.73e-09 ***
## `prdline.my.fctriPad 2` -34.644 11.324 -3.059 0.002250 **
## `prdline.my.fctriPad 3+` 37.734 11.148 3.385 0.000727 ***
## prdline.my.fctriPadAir 191.311 10.885 17.575 < 2e-16 ***
## prdline.my.fctriPadmini 4.683 11.398 0.411 0.681253
## `prdline.my.fctriPadmini 2+` 134.686 12.218 11.024 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 122.6 on 1851 degrees of freedom
## Multiple R-squared: 0.4844, Adjusted R-squared: 0.4824
## F-statistic: 248.4 on 7 and 1851 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.lm lm biddable, prdline.my.fctr 1
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 0.966 0.006 0.4843682
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit max.Rsquared.fit
## 1 122.7558 0.430036 130.8154 0.4824182 0.480809
## min.RMSESD.fit max.RsquaredSD.fit
## 1 3.627481 0.0327088if (!is.null(glb_date_vars) &&
(sum(grepl(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df))) > 0)) {
# ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly1",
# model_method=ifelse(glb_is_regression, "lm",
# ifelse(glb_is_binomial, "glm", "rpart")),
# model_type=glb_model_type,
# indep_vars_vctr=c(max_cor_y_x_vars, paste0(glb_date_vars, ".day.minutes")),
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
#
ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=c(max_cor_y_x_vars,
grep(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df), value=TRUE)),
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(unique(glb_feats_df$cor.high.X), NA)) > 0) {
# lm & glm handle interaction terms; rpart & rf do not
if (glb_is_regression || glb_is_binomial) {
indep_vars_vctr <-
c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":"))
} else { indep_vars_vctr <- union(max_cor_y_x_vars, int_feats) }
ret_lst <- myfit_mdl(model_id="Interact.High.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
} ## [1] "fitting model: Interact.High.cor.Y.lm"
## [1] " indep_vars: biddable, prdline.my.fctr, biddable:D.npnct24.log, biddable:carrier.fctr, biddable:D.npnct06.log, biddable:D.terms.n.post.stop, biddable:D.nstopwrds.log, biddable:D.TfIdf.sum.post.stop, biddable:D.terms.n.post.stop.log, biddable:D.nwrds.unq.log, biddable:D.nuppr.log, biddable:D.TfIdf.sum.post.stem"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -365.56 -67.07 -15.31 54.37 762.28
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.904 9.437 23.833 < 2e-16
## biddable -131.806 7.354 -17.923 < 2e-16
## `prdline.my.fctriPad 1` -68.693 12.344 -5.565 3.01e-08
## `prdline.my.fctriPad 2` -27.536 11.841 -2.325 0.020158
## `prdline.my.fctriPad 3+` 43.742 11.568 3.781 0.000161
## prdline.my.fctriPadAir 199.656 11.256 17.738 < 2e-16
## prdline.my.fctriPadmini 11.818 11.922 0.991 0.321699
## `prdline.my.fctriPadmini 2+` 141.033 12.528 11.257 < 2e-16
## `biddable:D.npnct24.log` -106.031 135.947 -0.780 0.435522
## `biddable:carrier.fctrAT&T` 7.438 14.724 0.505 0.613537
## `biddable:carrier.fctrOther` 52.856 72.710 0.727 0.467353
## `biddable:carrier.fctrSprint` -125.554 43.788 -2.867 0.004187
## `biddable:carrier.fctrT-Mobile` -80.413 43.922 -1.831 0.067292
## `biddable:carrier.fctrUnknown` 25.117 12.172 2.064 0.039198
## `biddable:carrier.fctrVerizon` -4.604 17.676 -0.260 0.794549
## `biddable:D.npnct06.log` 54.686 27.924 1.958 0.050339
## `biddable:D.terms.n.post.stop` -11.527 10.989 -1.049 0.294306
## `biddable:D.nstopwrds.log` 21.504 17.199 1.250 0.211356
## `biddable:D.TfIdf.sum.post.stop` -36.612 25.302 -1.447 0.148068
## `biddable:D.terms.n.post.stop.log` 45.608 281.496 0.162 0.871307
## `biddable:D.nwrds.unq.log` 67.754 255.083 0.266 0.790564
## `biddable:D.nuppr.log` -20.688 47.148 -0.439 0.660867
## `biddable:D.TfIdf.sum.post.stem` 28.359 26.413 1.074 0.283104
##
## (Intercept) ***
## biddable ***
## `prdline.my.fctriPad 1` ***
## `prdline.my.fctriPad 2` *
## `prdline.my.fctriPad 3+` ***
## prdline.my.fctriPadAir ***
## prdline.my.fctriPadmini
## `prdline.my.fctriPadmini 2+` ***
## `biddable:D.npnct24.log`
## `biddable:carrier.fctrAT&T`
## `biddable:carrier.fctrOther`
## `biddable:carrier.fctrSprint` **
## `biddable:carrier.fctrT-Mobile` .
## `biddable:carrier.fctrUnknown` *
## `biddable:carrier.fctrVerizon`
## `biddable:D.npnct06.log` .
## `biddable:D.terms.n.post.stop`
## `biddable:D.nstopwrds.log`
## `biddable:D.TfIdf.sum.post.stop`
## `biddable:D.terms.n.post.stop.log`
## `biddable:D.nwrds.unq.log`
## `biddable:D.nuppr.log`
## `biddable:D.TfIdf.sum.post.stem`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 121.6 on 1836 degrees of freedom
## Multiple R-squared: 0.4965, Adjusted R-squared: 0.4905
## F-statistic: 82.29 on 22 and 1836 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 Interact.High.cor.Y.lm lm
## feats
## 1 biddable, prdline.my.fctr, biddable:D.npnct24.log, biddable:carrier.fctr, biddable:D.npnct06.log, biddable:D.terms.n.post.stop, biddable:D.nstopwrds.log, biddable:D.TfIdf.sum.post.stop, biddable:D.terms.n.post.stop.log, biddable:D.nwrds.unq.log, biddable:D.nuppr.log, biddable:D.TfIdf.sum.post.stem
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.956 0.014
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.4964956 122.1761 0.4278967 131.0606 0.4904624
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.4857043 4.465793 0.03984517# Low.cor.X
# if (glb_is_classification && glb_is_binomial)
# indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
# is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"] else
indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) & !myNearZV &
(exclude.as.feat != 1))[, "id"]
myadjust_interaction_feats <- function(vars_vctr) {
for (feat in subset(glb_feats_df, !is.na(interaction.feat))$id)
if (feat %in% vars_vctr)
vars_vctr <- union(setdiff(vars_vctr, feat),
paste0(glb_feats_df[glb_feats_df$id == feat, "interaction.feat"], ":",
feat))
return(vars_vctr)
}
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
ret_lst <- myfit_mdl(model_id="Low.cor.X",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Low.cor.X.lm"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.ratio.sum.TfIdf.nwrds, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -435.25 -49.79 -3.07 52.73 755.52
##
## Coefficients: (7 not defined because of singularities)
## Estimate Std. Error
## (Intercept) 2.296e+02 1.469e+02
## `prdline.my.fctriPad 1` -6.231e+01 1.404e+01
## `prdline.my.fctriPad 2` -8.329e+00 1.371e+01
## `prdline.my.fctriPad 3+` 4.149e+01 1.333e+01
## prdline.my.fctriPadAir 1.363e+02 1.303e+01
## prdline.my.fctriPadmini 1.667e+01 1.316e+01
## `prdline.my.fctriPadmini 2+` 7.242e+01 1.377e+01
## `condition.fctrFor parts or not working` -9.258e+01 9.810e+00
## `condition.fctrManufacturer refurbished` 2.703e+01 1.691e+01
## condition.fctrNew 8.912e+01 8.203e+00
## `condition.fctrNew other (see details)` 5.916e+01 1.205e+01
## `condition.fctrSeller refurbished` -1.174e+01 1.066e+01
## color.fctrBlack -4.069e+00 6.654e+00
## color.fctrGold 5.435e+01 1.336e+01
## `color.fctrSpace Gray` 1.197e+01 8.742e+00
## color.fctrWhite 1.140e+01 6.459e+00
## D.TfIdf.sum.stem.stop.Ratio 8.414e+01 8.332e+01
## idseq.my 2.709e-02 5.392e-03
## `carrier.fctrAT&T` 1.578e+01 8.012e+00
## carrier.fctrOther 2.037e+00 6.010e+01
## carrier.fctrSprint -1.530e+01 1.893e+01
## `carrier.fctrT-Mobile` 1.237e+01 2.374e+01
## carrier.fctrUnknown 2.242e+01 8.078e+00
## carrier.fctrVerizon 1.090e+01 9.462e+00
## D.npnct09.log 8.771e+01 3.317e+01
## D.npnct10.log 5.344e+01 5.204e+01
## D.terms.n.stem.stop.Ratio 6.139e+01 1.342e+02
## D.npnct28.log -6.769e+01 5.061e+01
## D.npnct14.log -1.576e+01 1.891e+01
## .rnorm -6.759e-01 2.405e+00
## D.npnct05.log -1.988e+01 3.230e+01
## D.npnct08.log -1.783e+01 1.755e+01
## D.npnct01.log 1.604e+01 1.517e+01
## D.ndgts.log 2.444e+00 1.030e+01
## D.npnct12.log 5.352e+00 1.583e+01
## D.npnct06.log -1.596e+00 2.170e+01
## D.npnct15.log -1.633e+01 2.955e+01
## D.npnct11.log -1.077e+01 8.672e+00
## D.npnct03.log 2.811e+01 3.785e+01
## storage.fctr16 -2.099e+02 1.193e+01
## storage.fctr32 -1.862e+02 1.287e+01
## storage.fctr64 -1.394e+02 1.259e+01
## storage.fctrUnknown -2.262e+02 1.563e+01
## D.npnct13.log -8.871e+00 8.298e+00
## D.ratio.sum.TfIdf.nwrds -1.656e+01 5.120e+00
## D.TfIdf.sum.post.stop 1.597e+00 1.920e+00
## biddable -1.179e+02 5.129e+00
## `prdline.my.fctrUnknown:.clusterid.fctr2` 4.028e+01 1.859e+01
## `prdline.my.fctriPad 1:.clusterid.fctr2` -1.368e+01 2.192e+01
## `prdline.my.fctriPad 2:.clusterid.fctr2` -3.392e+00 1.608e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 2.188e+01 1.754e+01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -3.743e+01 1.744e+01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 9.330e+00 2.086e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 3.272e+01 2.364e+01
## `prdline.my.fctrUnknown:.clusterid.fctr3` -4.759e+00 2.563e+01
## `prdline.my.fctriPad 1:.clusterid.fctr3` 8.366e+00 2.272e+01
## `prdline.my.fctriPad 2:.clusterid.fctr3` 1.018e+01 2.516e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 7.479e+00 1.915e+01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -1.421e+01 2.260e+01
## `prdline.my.fctriPadmini:.clusterid.fctr3` -6.693e+00 2.172e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -4.330e+00 2.606e+01
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 2.492e+01 2.494e+01
## `prdline.my.fctriPad 2:.clusterid.fctr4` -2.756e+01 2.646e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 9.693e+00 2.059e+01
## `prdline.my.fctriPadAir:.clusterid.fctr4` -1.887e+01 2.286e+01
## `prdline.my.fctriPadmini:.clusterid.fctr4` -8.225e+00 2.427e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 4.766e+00 2.970e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 3.795e+01 3.223e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## t value Pr(>|t|)
## (Intercept) 1.562 0.11837
## `prdline.my.fctriPad 1` -4.437 9.69e-06 ***
## `prdline.my.fctriPad 2` -0.608 0.54357
## `prdline.my.fctriPad 3+` 3.111 0.00189 **
## prdline.my.fctriPadAir 10.463 < 2e-16 ***
## prdline.my.fctriPadmini 1.267 0.20536
## `prdline.my.fctriPadmini 2+` 5.258 1.63e-07 ***
## `condition.fctrFor parts or not working` -9.437 < 2e-16 ***
## `condition.fctrManufacturer refurbished` 1.598 0.11027
## condition.fctrNew 10.865 < 2e-16 ***
## `condition.fctrNew other (see details)` 4.910 9.95e-07 ***
## `condition.fctrSeller refurbished` -1.101 0.27104
## color.fctrBlack -0.612 0.54090
## color.fctrGold 4.068 4.95e-05 ***
## `color.fctrSpace Gray` 1.369 0.17103
## color.fctrWhite 1.765 0.07767 .
## D.TfIdf.sum.stem.stop.Ratio 1.010 0.31271
## idseq.my 5.024 5.56e-07 ***
## `carrier.fctrAT&T` 1.970 0.04903 *
## carrier.fctrOther 0.034 0.97296
## carrier.fctrSprint -0.808 0.41912
## `carrier.fctrT-Mobile` 0.521 0.60239
## carrier.fctrUnknown 2.776 0.00557 **
## carrier.fctrVerizon 1.152 0.24962
## D.npnct09.log 2.644 0.00826 **
## D.npnct10.log 1.027 0.30458
## D.terms.n.stem.stop.Ratio 0.457 0.64741
## D.npnct28.log -1.337 0.18124
## D.npnct14.log -0.834 0.40465
## .rnorm -0.281 0.77875
## D.npnct05.log -0.615 0.53834
## D.npnct08.log -1.016 0.30955
## D.npnct01.log 1.057 0.29069
## D.ndgts.log 0.237 0.81243
## D.npnct12.log 0.338 0.73531
## D.npnct06.log -0.074 0.94137
## D.npnct15.log -0.553 0.58061
## D.npnct11.log -1.242 0.21443
## D.npnct03.log 0.743 0.45785
## storage.fctr16 -17.592 < 2e-16 ***
## storage.fctr32 -14.472 < 2e-16 ***
## storage.fctr64 -11.073 < 2e-16 ***
## storage.fctrUnknown -14.471 < 2e-16 ***
## D.npnct13.log -1.069 0.28520
## D.ratio.sum.TfIdf.nwrds -3.235 0.00124 **
## D.TfIdf.sum.post.stop 0.831 0.40583
## biddable -22.991 < 2e-16 ***
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.167 0.03039 *
## `prdline.my.fctriPad 1:.clusterid.fctr2` -0.624 0.53254
## `prdline.my.fctriPad 2:.clusterid.fctr2` -0.211 0.83289
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 1.247 0.21242
## `prdline.my.fctriPadAir:.clusterid.fctr2` -2.146 0.03201 *
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.447 0.65482
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 1.384 0.16653
## `prdline.my.fctrUnknown:.clusterid.fctr3` -0.186 0.85269
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.368 0.71276
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.405 0.68583
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.391 0.69615
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.629 0.52957
## `prdline.my.fctriPadmini:.clusterid.fctr3` -0.308 0.75803
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.166 0.86804
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 0.999 0.31769
## `prdline.my.fctriPad 2:.clusterid.fctr4` -1.042 0.29778
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.471 0.63790
## `prdline.my.fctriPadAir:.clusterid.fctr4` -0.826 0.40911
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.339 0.73474
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.160 0.87253
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 1.178 0.23909
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 100.5 on 1791 degrees of freedom
## Multiple R-squared: 0.6649, Adjusted R-squared: 0.6523
## F-statistic: 53.04 on 67 and 1791 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
## model_id model_method
## 1 Low.cor.X.lm lm
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.ratio.sum.TfIdf.nwrds, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.283 0.053
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.6648823 102.6309 0.5499382 116.2442 0.6523458
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.6367757 5.230233 0.03879193rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 10 fit.models 7 0 97.894 114.154 16.26
## 11 fit.models 7 1 114.154 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn")## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 116.771 NA NA# Options:
# 1. rpart & rf manual tuning
# 2. rf without pca (default: with pca)
#stop(here"); sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df
#glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
# All X that is not user excluded
for (model_id_pfx in c("All.X", "All.Interact.X")) {
#model_id_pfx <- "All.X"
indep_vars_vctr <- subset(glb_feats_df, !myNearZV &
(exclude.as.feat != 1))[, "id"]
if (model_id_pfx == "All.Interact.X") {
interact_vars_vctr <- c(
#"startprice.log",
"biddable", "idseq.my")
indep_vars_vctr <- union(setdiff(indep_vars_vctr, interact_vars_vctr),
paste(glb_category_var, interact_vars_vctr, sep=".fctr*"))
}
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
#stop(here")
for (method in glb_models_method_vctr) {
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", method), major.inc=TRUE)
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indep_vars_vctr <- setdiff(indep_vars_vctr, c(".rnorm"))
model_id <- paste0(model_id_pfx, ".no.rnorm")
} else model_id <- model_id_pfx
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# If All.X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indep_vars_vctr
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(model_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glb_fitobs_df[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE), ]
# all.equal(glb_allobs_df$S.nuppr.log, glb_allobs_df$A.nuppr.log)
# all.equal(glb_allobs_df$S.npnct19.log, glb_allobs_df$A.npnct19.log)
# all.equal(glb_allobs_df$S.P.year.colon, glb_allobs_df$A.P.year.colon)
# all.equal(glb_allobs_df$S.T.share, glb_allobs_df$A.T.share)
# all.equal(glb_allobs_df$H.T.clip, glb_allobs_df$H.P.daily.clip.report)
# cor(glb_allobs_df$S.T.herald, glb_allobs_df$S.T.tribun)
# dsp_obs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# dsp_obs(Abstract.contains="[Ss]hare", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glb_allobs_df[, setdiff(names(glb_allobs_df), myfind_chr_cols_df(glb_allobs_df))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".cp.0"), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
}
}## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 116.771 116.779 0.009
## 2 fit.models_1_lm 2 0 116.780 NA NA
## [1] "fitting model: All.X.lm"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -446.08 -49.34 -2.18 51.80 754.43
##
## Coefficients: (10 not defined because of singularities)
## Estimate Std. Error
## (Intercept) -5.206e+04 5.763e+04
## `prdline.my.fctriPad 1` -7.604e+01 1.434e+01
## `prdline.my.fctriPad 2` -1.869e+01 1.393e+01
## `prdline.my.fctriPad 3+` 2.989e+01 1.361e+01
## prdline.my.fctriPadAir 1.241e+02 1.334e+01
## prdline.my.fctriPadmini 4.250e+00 1.345e+01
## `prdline.my.fctriPadmini 2+` 6.139e+01 1.399e+01
## `condition.fctrFor parts or not working` -9.200e+01 9.827e+00
## `condition.fctrManufacturer refurbished` 2.488e+01 1.692e+01
## condition.fctrNew 8.703e+01 8.223e+00
## `condition.fctrNew other (see details)` 5.694e+01 1.217e+01
## `condition.fctrSeller refurbished` -1.400e+01 1.074e+01
## color.fctrBlack -7.856e+00 6.711e+00
## color.fctrGold 5.268e+01 1.333e+01
## `color.fctrSpace Gray` 9.689e+00 8.749e+00
## color.fctrWhite 8.957e+00 6.500e+00
## D.TfIdf.sum.stem.stop.Ratio 3.272e+02 4.590e+02
## D.ratio.nstopwrds.nwrds 1.527e+02 1.655e+02
## idseq.my 2.802e-02 5.403e-03
## `carrier.fctrAT&T` -5.172e+01 1.716e+01
## carrier.fctrOther -5.124e+01 6.248e+01
## carrier.fctrSprint -8.336e+01 2.416e+01
## `carrier.fctrT-Mobile` -5.587e+01 2.840e+01
## carrier.fctrUnknown -1.898e+01 1.229e+01
## carrier.fctrVerizon -5.564e+01 1.783e+01
## D.npnct09.log 8.171e+01 3.433e+01
## D.npnct10.log 4.878e+01 5.216e+01
## D.terms.n.stem.stop.Ratio 5.196e+04 5.761e+04
## D.npnct28.log -6.178e+01 5.081e+01
## cellular.fctr1 6.894e+01 1.544e+01
## cellular.fctrUnknown NA NA
## D.npnct14.log -2.069e+01 1.913e+01
## .rnorm -1.833e-01 2.405e+00
## D.npnct05.log -1.793e+01 3.440e+01
## D.npnct08.log -2.128e+01 1.783e+01
## D.npnct01.log 1.603e+01 1.551e+01
## D.ndgts.log -4.986e+00 1.221e+01
## D.npnct12.log -4.389e+00 1.652e+01
## D.npnct16.log 1.061e+01 5.028e+01
## D.npnct06.log -1.360e+01 5.466e+01
## D.npnct15.log -1.979e+01 2.980e+01
## D.npnct11.log -1.227e+01 9.228e+00
## D.npnct03.log 2.942e+01 4.117e+01
## storage.fctr16 -2.032e+02 1.210e+01
## storage.fctr32 -1.811e+02 1.300e+01
## storage.fctr64 -1.356e+02 1.265e+01
## storage.fctrUnknown -1.927e+02 1.758e+01
## D.npnct13.log -8.650e+00 9.315e+00
## D.terms.n.post.stem 3.967e+02 3.807e+02
## D.terms.n.post.stop -3.970e+02 3.799e+02
## D.ratio.sum.TfIdf.nwrds -1.101e+01 1.038e+01
## D.nstopwrds.log -7.249e+01 4.838e+01
## D.nwrds.unq.log -5.858e+04 6.441e+04
## D.terms.n.post.stem.log NA NA
## D.terms.n.post.stop.log 5.857e+04 6.440e+04
## D.nwrds.log 7.454e+01 6.777e+01
## D.nchrs.log 8.086e+01 6.970e+01
## D.nuppr.log -6.149e+01 5.239e+01
## D.npnct24.log -5.152e+01 1.357e+02
## D.TfIdf.sum.post.stem -4.248e+01 7.142e+01
## D.sum.TfIdf NA NA
## D.TfIdf.sum.post.stop 4.207e+01 6.852e+01
## biddable -1.182e+02 5.146e+00
## `prdline.my.fctrUnknown:.clusterid.fctr2` 4.163e+01 1.962e+01
## `prdline.my.fctriPad 1:.clusterid.fctr2` -9.915e+00 2.283e+01
## `prdline.my.fctriPad 2:.clusterid.fctr2` -2.758e+00 1.693e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 2.081e+01 1.867e+01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -3.887e+01 1.823e+01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.139e+01 2.185e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 3.219e+01 2.444e+01
## `prdline.my.fctrUnknown:.clusterid.fctr3` -4.775e+00 2.668e+01
## `prdline.my.fctriPad 1:.clusterid.fctr3` 1.181e+01 2.371e+01
## `prdline.my.fctriPad 2:.clusterid.fctr3` 2.042e+01 2.655e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 7.564e+00 1.986e+01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -1.469e+01 2.322e+01
## `prdline.my.fctriPadmini:.clusterid.fctr3` -2.496e+00 2.244e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -7.202e+00 2.657e+01
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 2.958e+01 2.583e+01
## `prdline.my.fctriPad 2:.clusterid.fctr4` -2.777e+01 2.817e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 8.021e+00 2.128e+01
## `prdline.my.fctriPadAir:.clusterid.fctr4` -1.263e+01 2.475e+01
## `prdline.my.fctriPadmini:.clusterid.fctr4` -6.076e+00 2.518e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 1.372e+01 3.086e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 3.887e+01 3.305e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## t value Pr(>|t|)
## (Intercept) -0.903 0.366429
## `prdline.my.fctriPad 1` -5.303 1.28e-07 ***
## `prdline.my.fctriPad 2` -1.341 0.180117
## `prdline.my.fctriPad 3+` 2.196 0.028212 *
## prdline.my.fctriPadAir 9.297 < 2e-16 ***
## prdline.my.fctriPadmini 0.316 0.751990
## `prdline.my.fctriPadmini 2+` 4.389 1.21e-05 ***
## `condition.fctrFor parts or not working` -9.362 < 2e-16 ***
## `condition.fctrManufacturer refurbished` 1.471 0.141586
## condition.fctrNew 10.583 < 2e-16 ***
## `condition.fctrNew other (see details)` 4.678 3.11e-06 ***
## `condition.fctrSeller refurbished` -1.303 0.192615
## color.fctrBlack -1.171 0.241895
## color.fctrGold 3.953 8.02e-05 ***
## `color.fctrSpace Gray` 1.107 0.268240
## color.fctrWhite 1.378 0.168349
## D.TfIdf.sum.stem.stop.Ratio 0.713 0.476008
## D.ratio.nstopwrds.nwrds 0.923 0.356350
## idseq.my 5.186 2.40e-07 ***
## `carrier.fctrAT&T` -3.014 0.002613 **
## carrier.fctrOther -0.820 0.412275
## carrier.fctrSprint -3.450 0.000574 ***
## `carrier.fctrT-Mobile` -1.968 0.049263 *
## carrier.fctrUnknown -1.545 0.122630
## carrier.fctrVerizon -3.121 0.001834 **
## D.npnct09.log 2.380 0.017403 *
## D.npnct10.log 0.935 0.349811
## D.terms.n.stem.stop.Ratio 0.902 0.367212
## D.npnct28.log -1.216 0.224145
## cellular.fctr1 4.465 8.51e-06 ***
## cellular.fctrUnknown NA NA
## D.npnct14.log -1.082 0.279545
## .rnorm -0.076 0.939254
## D.npnct05.log -0.521 0.602256
## D.npnct08.log -1.194 0.232816
## D.npnct01.log 1.033 0.301514
## D.ndgts.log -0.408 0.683088
## D.npnct12.log -0.266 0.790499
## D.npnct16.log 0.211 0.832838
## D.npnct06.log -0.249 0.803542
## D.npnct15.log -0.664 0.506735
## D.npnct11.log -1.330 0.183804
## D.npnct03.log 0.715 0.474846
## storage.fctr16 -16.799 < 2e-16 ***
## storage.fctr32 -13.931 < 2e-16 ***
## storage.fctr64 -10.721 < 2e-16 ***
## storage.fctrUnknown -10.958 < 2e-16 ***
## D.npnct13.log -0.929 0.353207
## D.terms.n.post.stem 1.042 0.297572
## D.terms.n.post.stop -1.045 0.296126
## D.ratio.sum.TfIdf.nwrds -1.060 0.289101
## D.nstopwrds.log -1.498 0.134204
## D.nwrds.unq.log -0.910 0.363152
## D.terms.n.post.stem.log NA NA
## D.terms.n.post.stop.log 0.909 0.363212
## D.nwrds.log 1.100 0.271517
## D.nchrs.log 1.160 0.246155
## D.nuppr.log -1.174 0.240695
## D.npnct24.log -0.380 0.704322
## D.TfIdf.sum.post.stem -0.595 0.552023
## D.sum.TfIdf NA NA
## D.TfIdf.sum.post.stop 0.614 0.539281
## biddable -22.963 < 2e-16 ***
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.122 0.033976 *
## `prdline.my.fctriPad 1:.clusterid.fctr2` -0.434 0.664076
## `prdline.my.fctriPad 2:.clusterid.fctr2` -0.163 0.870639
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 1.115 0.265000
## `prdline.my.fctriPadAir:.clusterid.fctr2` -2.133 0.033075 *
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.521 0.602264
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 1.317 0.188031
## `prdline.my.fctrUnknown:.clusterid.fctr3` -0.179 0.858012
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.498 0.618291
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.769 0.441883
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.381 0.703334
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.633 0.526984
## `prdline.my.fctriPadmini:.clusterid.fctr3` -0.111 0.911447
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.271 0.786387
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 1.145 0.252386
## `prdline.my.fctriPad 2:.clusterid.fctr4` -0.986 0.324393
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.377 0.706272
## `prdline.my.fctriPadAir:.clusterid.fctr4` -0.510 0.609816
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.241 0.809349
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.444 0.656781
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 1.176 0.239703
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 100.1 on 1778 degrees of freedom
## Multiple R-squared: 0.6699, Adjusted R-squared: 0.655
## F-statistic: 45.1 on 80 and 1778 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
## model_id model_method
## 1 All.X.lm lm
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.196 0.063
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.6699012 102.1968 0.5509504 116.1134 0.6550486
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.6397998 5.770372 0.04332778
## label step_major step_minor bgn end elapsed
## 2 fit.models_1_lm 2 0 116.780 119.906 3.126
## 3 fit.models_1_glm 3 0 119.906 NA NA
## [1] "fitting model: All.X.glm"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -446.08 -49.34 -2.18 51.80 754.43
##
## Coefficients: (10 not defined because of singularities)
## Estimate Std. Error
## (Intercept) -5.206e+04 5.763e+04
## `prdline.my.fctriPad 1` -7.604e+01 1.434e+01
## `prdline.my.fctriPad 2` -1.869e+01 1.393e+01
## `prdline.my.fctriPad 3+` 2.989e+01 1.361e+01
## prdline.my.fctriPadAir 1.241e+02 1.334e+01
## prdline.my.fctriPadmini 4.250e+00 1.345e+01
## `prdline.my.fctriPadmini 2+` 6.139e+01 1.399e+01
## `condition.fctrFor parts or not working` -9.200e+01 9.827e+00
## `condition.fctrManufacturer refurbished` 2.488e+01 1.692e+01
## condition.fctrNew 8.703e+01 8.223e+00
## `condition.fctrNew other (see details)` 5.694e+01 1.217e+01
## `condition.fctrSeller refurbished` -1.400e+01 1.074e+01
## color.fctrBlack -7.856e+00 6.711e+00
## color.fctrGold 5.268e+01 1.333e+01
## `color.fctrSpace Gray` 9.689e+00 8.749e+00
## color.fctrWhite 8.957e+00 6.500e+00
## D.TfIdf.sum.stem.stop.Ratio 3.272e+02 4.590e+02
## D.ratio.nstopwrds.nwrds 1.527e+02 1.655e+02
## idseq.my 2.802e-02 5.403e-03
## `carrier.fctrAT&T` -5.172e+01 1.716e+01
## carrier.fctrOther -5.124e+01 6.248e+01
## carrier.fctrSprint -8.336e+01 2.416e+01
## `carrier.fctrT-Mobile` -5.587e+01 2.840e+01
## carrier.fctrUnknown -1.898e+01 1.229e+01
## carrier.fctrVerizon -5.564e+01 1.783e+01
## D.npnct09.log 8.171e+01 3.433e+01
## D.npnct10.log 4.878e+01 5.216e+01
## D.terms.n.stem.stop.Ratio 5.196e+04 5.761e+04
## D.npnct28.log -6.178e+01 5.081e+01
## cellular.fctr1 6.894e+01 1.544e+01
## cellular.fctrUnknown NA NA
## D.npnct14.log -2.069e+01 1.913e+01
## .rnorm -1.833e-01 2.405e+00
## D.npnct05.log -1.793e+01 3.440e+01
## D.npnct08.log -2.128e+01 1.783e+01
## D.npnct01.log 1.603e+01 1.551e+01
## D.ndgts.log -4.986e+00 1.221e+01
## D.npnct12.log -4.389e+00 1.652e+01
## D.npnct16.log 1.061e+01 5.028e+01
## D.npnct06.log -1.360e+01 5.466e+01
## D.npnct15.log -1.979e+01 2.980e+01
## D.npnct11.log -1.227e+01 9.228e+00
## D.npnct03.log 2.942e+01 4.117e+01
## storage.fctr16 -2.032e+02 1.210e+01
## storage.fctr32 -1.811e+02 1.300e+01
## storage.fctr64 -1.356e+02 1.265e+01
## storage.fctrUnknown -1.927e+02 1.758e+01
## D.npnct13.log -8.650e+00 9.315e+00
## D.terms.n.post.stem 3.967e+02 3.807e+02
## D.terms.n.post.stop -3.970e+02 3.799e+02
## D.ratio.sum.TfIdf.nwrds -1.101e+01 1.038e+01
## D.nstopwrds.log -7.249e+01 4.838e+01
## D.nwrds.unq.log -5.858e+04 6.441e+04
## D.terms.n.post.stem.log NA NA
## D.terms.n.post.stop.log 5.857e+04 6.440e+04
## D.nwrds.log 7.454e+01 6.777e+01
## D.nchrs.log 8.086e+01 6.970e+01
## D.nuppr.log -6.149e+01 5.239e+01
## D.npnct24.log -5.152e+01 1.357e+02
## D.TfIdf.sum.post.stem -4.248e+01 7.142e+01
## D.sum.TfIdf NA NA
## D.TfIdf.sum.post.stop 4.207e+01 6.852e+01
## biddable -1.182e+02 5.146e+00
## `prdline.my.fctrUnknown:.clusterid.fctr2` 4.163e+01 1.962e+01
## `prdline.my.fctriPad 1:.clusterid.fctr2` -9.915e+00 2.283e+01
## `prdline.my.fctriPad 2:.clusterid.fctr2` -2.758e+00 1.693e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 2.081e+01 1.867e+01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -3.887e+01 1.823e+01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.139e+01 2.185e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 3.219e+01 2.444e+01
## `prdline.my.fctrUnknown:.clusterid.fctr3` -4.775e+00 2.668e+01
## `prdline.my.fctriPad 1:.clusterid.fctr3` 1.181e+01 2.371e+01
## `prdline.my.fctriPad 2:.clusterid.fctr3` 2.042e+01 2.655e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 7.564e+00 1.986e+01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -1.469e+01 2.322e+01
## `prdline.my.fctriPadmini:.clusterid.fctr3` -2.496e+00 2.244e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -7.202e+00 2.657e+01
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 2.958e+01 2.583e+01
## `prdline.my.fctriPad 2:.clusterid.fctr4` -2.777e+01 2.817e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 8.021e+00 2.128e+01
## `prdline.my.fctriPadAir:.clusterid.fctr4` -1.263e+01 2.475e+01
## `prdline.my.fctriPadmini:.clusterid.fctr4` -6.076e+00 2.518e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 1.372e+01 3.086e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 3.887e+01 3.305e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## t value Pr(>|t|)
## (Intercept) -0.903 0.366429
## `prdline.my.fctriPad 1` -5.303 1.28e-07 ***
## `prdline.my.fctriPad 2` -1.341 0.180117
## `prdline.my.fctriPad 3+` 2.196 0.028212 *
## prdline.my.fctriPadAir 9.297 < 2e-16 ***
## prdline.my.fctriPadmini 0.316 0.751990
## `prdline.my.fctriPadmini 2+` 4.389 1.21e-05 ***
## `condition.fctrFor parts or not working` -9.362 < 2e-16 ***
## `condition.fctrManufacturer refurbished` 1.471 0.141586
## condition.fctrNew 10.583 < 2e-16 ***
## `condition.fctrNew other (see details)` 4.678 3.11e-06 ***
## `condition.fctrSeller refurbished` -1.303 0.192615
## color.fctrBlack -1.171 0.241895
## color.fctrGold 3.953 8.02e-05 ***
## `color.fctrSpace Gray` 1.107 0.268240
## color.fctrWhite 1.378 0.168349
## D.TfIdf.sum.stem.stop.Ratio 0.713 0.476008
## D.ratio.nstopwrds.nwrds 0.923 0.356350
## idseq.my 5.186 2.40e-07 ***
## `carrier.fctrAT&T` -3.014 0.002613 **
## carrier.fctrOther -0.820 0.412275
## carrier.fctrSprint -3.450 0.000574 ***
## `carrier.fctrT-Mobile` -1.968 0.049263 *
## carrier.fctrUnknown -1.545 0.122630
## carrier.fctrVerizon -3.121 0.001834 **
## D.npnct09.log 2.380 0.017403 *
## D.npnct10.log 0.935 0.349811
## D.terms.n.stem.stop.Ratio 0.902 0.367212
## D.npnct28.log -1.216 0.224145
## cellular.fctr1 4.465 8.51e-06 ***
## cellular.fctrUnknown NA NA
## D.npnct14.log -1.082 0.279545
## .rnorm -0.076 0.939254
## D.npnct05.log -0.521 0.602256
## D.npnct08.log -1.194 0.232816
## D.npnct01.log 1.033 0.301514
## D.ndgts.log -0.408 0.683088
## D.npnct12.log -0.266 0.790499
## D.npnct16.log 0.211 0.832838
## D.npnct06.log -0.249 0.803542
## D.npnct15.log -0.664 0.506735
## D.npnct11.log -1.330 0.183804
## D.npnct03.log 0.715 0.474846
## storage.fctr16 -16.799 < 2e-16 ***
## storage.fctr32 -13.931 < 2e-16 ***
## storage.fctr64 -10.721 < 2e-16 ***
## storage.fctrUnknown -10.958 < 2e-16 ***
## D.npnct13.log -0.929 0.353207
## D.terms.n.post.stem 1.042 0.297572
## D.terms.n.post.stop -1.045 0.296126
## D.ratio.sum.TfIdf.nwrds -1.060 0.289101
## D.nstopwrds.log -1.498 0.134204
## D.nwrds.unq.log -0.910 0.363152
## D.terms.n.post.stem.log NA NA
## D.terms.n.post.stop.log 0.909 0.363212
## D.nwrds.log 1.100 0.271517
## D.nchrs.log 1.160 0.246155
## D.nuppr.log -1.174 0.240695
## D.npnct24.log -0.380 0.704322
## D.TfIdf.sum.post.stem -0.595 0.552023
## D.sum.TfIdf NA NA
## D.TfIdf.sum.post.stop 0.614 0.539281
## biddable -22.963 < 2e-16 ***
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.122 0.033976 *
## `prdline.my.fctriPad 1:.clusterid.fctr2` -0.434 0.664076
## `prdline.my.fctriPad 2:.clusterid.fctr2` -0.163 0.870639
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 1.115 0.265000
## `prdline.my.fctriPadAir:.clusterid.fctr2` -2.133 0.033075 *
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.521 0.602264
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 1.317 0.188031
## `prdline.my.fctrUnknown:.clusterid.fctr3` -0.179 0.858012
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.498 0.618291
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.769 0.441883
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.381 0.703334
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.633 0.526984
## `prdline.my.fctriPadmini:.clusterid.fctr3` -0.111 0.911447
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.271 0.786387
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 1.145 0.252386
## `prdline.my.fctriPad 2:.clusterid.fctr4` -0.986 0.324393
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.377 0.706272
## `prdline.my.fctriPadAir:.clusterid.fctr4` -0.510 0.609816
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.241 0.809349
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.444 0.656781
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 1.176 0.239703
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 10015.99)
##
## Null deviance: 53948796 on 1858 degrees of freedom
## Residual deviance: 17808435 on 1778 degrees of freedom
## AIC: 22482
##
## Number of Fisher Scoring iterations: 2
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## model_id model_method
## 1 All.X.glm glm
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.302 0.102
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB min.aic.fit
## 1 0.6699012 102.1968 0.5509504 116.1134 22481.79
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.6397998 5.770372 0.04332778
## label step_major step_minor bgn end elapsed
## 3 fit.models_1_glm 3 0 119.906 123.215 3.309
## 4 fit.models_1_bayesglm 4 0 123.215 NA NA
## [1] "fitting model: All.X.bayesglm"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr"## Loading required package: arm
## Loading required package: MASS
##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## select
##
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
##
## The following object is masked from 'package:tidyr':
##
## expand
##
## Loading required package: lme4
##
## Attaching package: 'lme4'
##
## The following object is masked from 'package:nlme':
##
## lmList
##
##
## arm (Version 1.8-6, built: 2015-7-7)
##
## Working directory is /Users/bbalaji-2012/Documents/Work/Courses/MIT/Analytics_Edge_15_071x/Assignments/Kaggle_eBay_iPads## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -445.97 -48.79 -2.13 52.16 754.21
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 1.902e+02 6.944e+02
## `prdline.my.fctriPad 1` -7.618e+01 1.437e+01
## `prdline.my.fctriPad 2` -1.876e+01 1.396e+01
## `prdline.my.fctriPad 3+` 2.998e+01 1.363e+01
## prdline.my.fctriPadAir 1.241e+02 1.337e+01
## prdline.my.fctriPadmini 4.255e+00 1.347e+01
## `prdline.my.fctriPadmini 2+` 6.153e+01 1.402e+01
## `condition.fctrFor parts or not working` -9.211e+01 9.854e+00
## `condition.fctrManufacturer refurbished` 2.525e+01 1.696e+01
## condition.fctrNew 8.700e+01 8.246e+00
## `condition.fctrNew other (see details)` 5.656e+01 1.219e+01
## `condition.fctrSeller refurbished` -1.425e+01 1.075e+01
## color.fctrBlack -7.690e+00 6.729e+00
## color.fctrGold 5.277e+01 1.336e+01
## `color.fctrSpace Gray` 9.790e+00 8.769e+00
## color.fctrWhite 8.778e+00 6.504e+00
## D.TfIdf.sum.stem.stop.Ratio 2.166e+02 3.787e+02
## D.ratio.nstopwrds.nwrds 1.400e+02 1.585e+02
## idseq.my 2.812e-02 5.418e-03
## `carrier.fctrAT&T` -3.280e+00 2.212e+02
## carrier.fctrOther -3.970e+00 2.272e+02
## carrier.fctrSprint -3.455e+01 2.217e+02
## `carrier.fctrT-Mobile` -7.817e+00 2.220e+02
## carrier.fctrUnknown 2.947e+01 2.212e+02
## carrier.fctrVerizon -7.822e+00 2.212e+02
## D.npnct09.log 8.148e+01 3.429e+01
## D.npnct10.log 4.879e+01 5.212e+01
## D.terms.n.stem.stop.Ratio -1.654e+02 5.686e+02
## D.npnct28.log -6.170e+01 5.071e+01
## cellular.fctr1 2.044e+01 2.211e+02
## cellular.fctrUnknown -4.851e+01 2.214e+02
## D.npnct14.log -1.979e+01 1.912e+01
## .rnorm -2.009e-01 2.412e+00
## D.npnct05.log -1.615e+01 3.429e+01
## D.npnct08.log -2.086e+01 1.787e+01
## D.npnct01.log 1.582e+01 1.554e+01
## D.ndgts.log -4.738e+00 1.217e+01
## D.npnct12.log -3.241e+00 1.652e+01
## D.npnct16.log 1.009e+01 5.006e+01
## D.npnct06.log -1.504e+01 5.441e+01
## D.npnct15.log -2.028e+01 2.982e+01
## D.npnct11.log -1.302e+01 9.211e+00
## D.npnct03.log 3.115e+01 4.109e+01
## storage.fctr16 -2.024e+02 1.211e+01
## storage.fctr32 -1.802e+02 1.301e+01
## storage.fctr64 -1.348e+02 1.267e+01
## storage.fctrUnknown -1.916e+02 1.761e+01
## D.npnct13.log -9.086e+00 9.307e+00
## D.terms.n.post.stem 4.006e+01 6.326e+01
## D.terms.n.post.stop -4.083e+01 6.258e+01
## D.ratio.sum.TfIdf.nwrds -1.136e+01 1.026e+01
## D.nstopwrds.log -6.637e+01 4.665e+01
## D.nwrds.unq.log -5.363e+01 5.796e+02
## D.terms.n.post.stem.log -5.363e+01 5.796e+02
## D.terms.n.post.stop.log 9.434e+01 5.243e+02
## D.nwrds.log 6.789e+01 6.650e+01
## D.nchrs.log 7.954e+01 6.899e+01
## D.nuppr.log -5.994e+01 5.197e+01
## D.npnct24.log -6.086e+01 1.308e+02
## D.TfIdf.sum.post.stem -1.241e+01 4.948e+02
## D.sum.TfIdf -1.241e+01 4.948e+02
## D.TfIdf.sum.post.stop 2.515e+01 5.691e+01
## biddable -1.180e+02 5.158e+00
## `prdline.my.fctrUnknown:.clusterid.fctr2` 4.195e+01 1.964e+01
## `prdline.my.fctriPad 1:.clusterid.fctr2` -9.409e+00 2.286e+01
## `prdline.my.fctriPad 2:.clusterid.fctr2` -2.349e+00 1.693e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 2.129e+01 1.869e+01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -3.899e+01 1.825e+01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.086e+01 2.188e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 3.369e+01 2.440e+01
## `prdline.my.fctrUnknown:.clusterid.fctr3` -4.793e+00 2.671e+01
## `prdline.my.fctriPad 1:.clusterid.fctr3` 1.327e+01 2.370e+01
## `prdline.my.fctriPad 2:.clusterid.fctr3` 1.922e+01 2.653e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 8.455e+00 1.986e+01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -1.299e+01 2.320e+01
## `prdline.my.fctriPadmini:.clusterid.fctr3` -2.703e+00 2.248e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -8.022e+00 2.660e+01
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.000e+00 8.549e+02
## `prdline.my.fctriPad 1:.clusterid.fctr4` 2.989e+01 2.587e+01
## `prdline.my.fctriPad 2:.clusterid.fctr4` -2.693e+01 2.817e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 9.171e+00 2.130e+01
## `prdline.my.fctriPadAir:.clusterid.fctr4` -1.126e+01 2.473e+01
## `prdline.my.fctriPadmini:.clusterid.fctr4` -5.502e+00 2.520e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.000e+00 8.549e+02
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.000e+00 8.549e+02
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.000e+00 8.549e+02
## `prdline.my.fctriPad 2:.clusterid.fctr5` 1.413e+01 3.087e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.000e+00 8.549e+02
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.000e+00 8.549e+02
## `prdline.my.fctriPadmini:.clusterid.fctr5` 4.046e+01 3.304e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.000e+00 8.549e+02
## t value Pr(>|t|)
## (Intercept) 0.274 0.7842
## `prdline.my.fctriPad 1` -5.302 1.29e-07 ***
## `prdline.my.fctriPad 2` -1.344 0.1792
## `prdline.my.fctriPad 3+` 2.200 0.0280 *
## prdline.my.fctriPadAir 9.286 < 2e-16 ***
## prdline.my.fctriPadmini 0.316 0.7522
## `prdline.my.fctriPadmini 2+` 4.390 1.20e-05 ***
## `condition.fctrFor parts or not working` -9.347 < 2e-16 ***
## `condition.fctrManufacturer refurbished` 1.489 0.1367
## condition.fctrNew 10.550 < 2e-16 ***
## `condition.fctrNew other (see details)` 4.639 3.76e-06 ***
## `condition.fctrSeller refurbished` -1.326 0.1851
## color.fctrBlack -1.143 0.2533
## color.fctrGold 3.950 8.13e-05 ***
## `color.fctrSpace Gray` 1.116 0.2644
## color.fctrWhite 1.350 0.1773
## D.TfIdf.sum.stem.stop.Ratio 0.572 0.5673
## D.ratio.nstopwrds.nwrds 0.883 0.3773
## idseq.my 5.191 2.34e-07 ***
## `carrier.fctrAT&T` -0.015 0.9882
## carrier.fctrOther -0.017 0.9861
## carrier.fctrSprint -0.156 0.8762
## `carrier.fctrT-Mobile` -0.035 0.9719
## carrier.fctrUnknown 0.133 0.8940
## carrier.fctrVerizon -0.035 0.9718
## D.npnct09.log 2.376 0.0176 *
## D.npnct10.log 0.936 0.3493
## D.terms.n.stem.stop.Ratio -0.291 0.7712
## D.npnct28.log -1.217 0.2238
## cellular.fctr1 0.092 0.9264
## cellular.fctrUnknown -0.219 0.8266
## D.npnct14.log -1.035 0.3009
## .rnorm -0.083 0.9337
## D.npnct05.log -0.471 0.6378
## D.npnct08.log -1.167 0.2432
## D.npnct01.log 1.018 0.3089
## D.ndgts.log -0.389 0.6972
## D.npnct12.log -0.196 0.8445
## D.npnct16.log 0.202 0.8403
## D.npnct06.log -0.276 0.7823
## D.npnct15.log -0.680 0.4964
## D.npnct11.log -1.413 0.1578
## D.npnct03.log 0.758 0.4486
## storage.fctr16 -16.713 < 2e-16 ***
## storage.fctr32 -13.844 < 2e-16 ***
## storage.fctr64 -10.645 < 2e-16 ***
## storage.fctrUnknown -10.883 < 2e-16 ***
## D.npnct13.log -0.976 0.3290
## D.terms.n.post.stem 0.633 0.5267
## D.terms.n.post.stop -0.652 0.5142
## D.ratio.sum.TfIdf.nwrds -1.107 0.2686
## D.nstopwrds.log -1.423 0.1550
## D.nwrds.unq.log -0.093 0.9263
## D.terms.n.post.stem.log -0.093 0.9263
## D.terms.n.post.stop.log 0.180 0.8572
## D.nwrds.log 1.021 0.3075
## D.nchrs.log 1.153 0.2491
## D.nuppr.log -1.153 0.2490
## D.npnct24.log -0.465 0.6418
## D.TfIdf.sum.post.stem -0.025 0.9800
## D.sum.TfIdf -0.025 0.9800
## D.TfIdf.sum.post.stop 0.442 0.6586
## biddable -22.881 < 2e-16 ***
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.136 0.0328 *
## `prdline.my.fctriPad 1:.clusterid.fctr2` -0.412 0.6807
## `prdline.my.fctriPad 2:.clusterid.fctr2` -0.139 0.8897
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 1.139 0.2549
## `prdline.my.fctriPadAir:.clusterid.fctr2` -2.136 0.0328 *
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.496 0.6198
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 1.381 0.1676
## `prdline.my.fctrUnknown:.clusterid.fctr3` -0.179 0.8576
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.560 0.5756
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.724 0.4690
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.426 0.6703
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.560 0.5758
## `prdline.my.fctriPadmini:.clusterid.fctr3` -0.120 0.9043
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.302 0.7630
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.000 1.0000
## `prdline.my.fctriPad 1:.clusterid.fctr4` 1.156 0.2480
## `prdline.my.fctriPad 2:.clusterid.fctr4` -0.956 0.3393
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.431 0.6668
## `prdline.my.fctriPadAir:.clusterid.fctr4` -0.456 0.6488
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.218 0.8272
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.000 1.0000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.000 1.0000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.000 1.0000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.458 0.6472
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.000 1.0000
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.000 1.0000
## `prdline.my.fctriPadmini:.clusterid.fctr5` 1.224 0.2210
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.000 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 10077.96)
##
## Null deviance: 53948796 on 1858 degrees of freedom
## Residual deviance: 17817836 on 1768 degrees of freedom
## AIC: 22503
##
## Number of Fisher Scoring iterations: 12
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.X.bayesglm bayesglm
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 2.998 0.745
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB min.aic.fit
## 1 0.6697269 102.131 0.5514322 116.0511 22502.77
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.6402516 5.646925 0.04237281
## label step_major step_minor bgn end elapsed
## 4 fit.models_1_bayesglm 4 0 123.215 127.357 4.142
## 5 fit.models_1_glmnet 5 0 127.358 NA NA
## [1] "fitting model: All.X.glmnet"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr"## Loading required package: glmnet
## Loaded glmnet 2.0-2
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 1.55 on full training set
## Length Class Mode
## a0 94 -none- numeric
## beta 8460 dgCMatrix S4
## df 94 -none- numeric
## dim 2 -none- numeric
## lambda 94 -none- numeric
## dev.ratio 94 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 90 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 294.78257637
## prdline.my.fctriPad 1
## -76.71045424
## prdline.my.fctriPad 2
## -23.64322090
## prdline.my.fctriPad 3+
## 24.95136901
## prdline.my.fctriPadAir
## 119.79418098
## prdline.my.fctriPadmini 2+
## 57.44456493
## condition.fctrFor parts or not working
## -88.58209077
## condition.fctrManufacturer refurbished
## 20.69027409
## condition.fctrNew
## 90.44265248
## condition.fctrNew other (see details)
## 55.84012240
## condition.fctrSeller refurbished
## -9.35222586
## color.fctrBlack
## -7.46170465
## color.fctrGold
## 50.76406013
## color.fctrSpace Gray
## 6.39766654
## color.fctrWhite
## 6.80464281
## D.TfIdf.sum.stem.stop.Ratio
## 25.43746154
## idseq.my
## 0.02633208
## carrier.fctrSprint
## -16.90344697
## carrier.fctrUnknown
## 30.88068200
## D.npnct09.log
## 71.33981931
## D.npnct10.log
## 32.00751631
## D.terms.n.stem.stop.Ratio
## 27.42161833
## D.npnct28.log
## -31.23070785
## cellular.fctr1
## 14.88145023
## cellular.fctrUnknown
## -49.84674880
## D.npnct14.log
## -6.44241930
## D.npnct05.log
## -11.72906659
## D.npnct08.log
## -6.83561783
## D.npnct01.log
## 13.77599628
## D.npnct15.log
## -8.60514295
## D.npnct11.log
## -7.59188211
## D.npnct03.log
## 21.92795604
## storage.fctr16
## -162.98155887
## storage.fctr32
## -138.23288130
## storage.fctr64
## -94.74980540
## storage.fctrUnknown
## -151.03929961
## D.npnct13.log
## -2.99822888
## D.ratio.sum.TfIdf.nwrds
## -12.14704821
## biddable
## -116.86488285
## prdline.my.fctrUnknown:.clusterid.fctr2
## 26.62965681
## prdline.my.fctriPad 1:.clusterid.fctr2
## -10.22155294
## prdline.my.fctriPad 2:.clusterid.fctr2
## -2.72772819
## prdline.my.fctriPad 3+:.clusterid.fctr2
## 14.53079907
## prdline.my.fctriPadAir:.clusterid.fctr2
## -31.90398968
## prdline.my.fctriPadmini:.clusterid.fctr2
## 0.88956465
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 27.16155447
## prdline.my.fctrUnknown:.clusterid.fctr3
## -5.97461085
## prdline.my.fctriPadAir:.clusterid.fctr3
## -1.17994114
## prdline.my.fctriPadmini 2+:.clusterid.fctr3
## -0.66788627
## prdline.my.fctriPad 1:.clusterid.fctr4
## 11.38256051
## prdline.my.fctriPad 2:.clusterid.fctr4
## -22.79555224
## prdline.my.fctriPad 3+:.clusterid.fctr4
## 2.82723340
## prdline.my.fctriPadmini:.clusterid.fctr5
## 25.38093863
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 1.998775e+02
## prdline.my.fctriPad 1
## -7.644313e+01
## prdline.my.fctriPad 2
## -1.999249e+01
## prdline.my.fctriPad 3+
## 2.914958e+01
## prdline.my.fctriPadAir
## 1.234229e+02
## prdline.my.fctriPadmini
## 3.465888e+00
## prdline.my.fctriPadmini 2+
## 6.086067e+01
## condition.fctrFor parts or not working
## -9.227346e+01
## condition.fctrManufacturer refurbished
## 2.554944e+01
## condition.fctrNew
## 8.687130e+01
## condition.fctrNew other (see details)
## 5.599857e+01
## condition.fctrSeller refurbished
## -1.402468e+01
## color.fctrBlack
## -7.412825e+00
## color.fctrGold
## 5.278243e+01
## color.fctrSpace Gray
## 1.063730e+01
## color.fctrWhite
## 9.549012e+00
## D.TfIdf.sum.stem.stop.Ratio
## 6.591341e+01
## D.ratio.nstopwrds.nwrds
## 2.869129e+01
## idseq.my
## 2.824860e-02
## carrier.fctrOther
## -9.326523e+00
## carrier.fctrSprint
## -3.086106e+01
## carrier.fctrT-Mobile
## -4.973113e+00
## carrier.fctrUnknown
## 3.307679e+01
## carrier.fctrVerizon
## -4.757925e+00
## D.npnct09.log
## 9.151029e+01
## D.npnct10.log
## 5.383163e+01
## D.terms.n.stem.stop.Ratio
## 8.575788e+01
## D.npnct28.log
## -6.770364e+01
## cellular.fctr1
## 1.701378e+01
## cellular.fctrUnknown
## -5.221195e+01
## D.npnct14.log
## -1.732644e+01
## .rnorm
## -2.606380e-01
## D.npnct05.log
## -1.292165e+01
## D.npnct08.log
## -1.823787e+01
## D.npnct01.log
## 1.689144e+01
## D.ndgts.log
## -1.668628e+00
## D.npnct12.log
## 2.053090e+00
## D.npnct16.log
## 1.070520e+01
## D.npnct06.log
## -1.535096e+01
## D.npnct15.log
## -2.019071e+01
## D.npnct11.log
## -1.121243e+01
## D.npnct03.log
## 3.598661e+01
## storage.fctr16
## -2.008292e+02
## storage.fctr32
## -1.783456e+02
## storage.fctr64
## -1.335379e+02
## storage.fctrUnknown
## -1.901398e+02
## D.npnct13.log
## -7.450029e+00
## D.terms.n.post.stop
## -1.798417e+00
## D.ratio.sum.TfIdf.nwrds
## -1.186745e+01
## D.nstopwrds.log
## -2.528831e+01
## D.nwrds.unq.log
## -2.345913e+00
## D.terms.n.post.stem.log
## -1.005925e-02
## D.terms.n.post.stop.log
## -1.337724e-03
## D.nwrds.log
## 4.736143e+01
## D.nchrs.log
## 3.200722e+00
## D.nuppr.log
## -7.287495e+00
## D.npnct24.log
## -5.018553e+01
## D.TfIdf.sum.post.stop
## 1.644332e+00
## biddable
## -1.180910e+02
## prdline.my.fctrUnknown:.clusterid.fctr2
## 4.104686e+01
## prdline.my.fctriPad 1:.clusterid.fctr2
## -8.957112e+00
## prdline.my.fctriPad 2:.clusterid.fctr2
## -1.877347e+00
## prdline.my.fctriPad 3+:.clusterid.fctr2
## 2.133655e+01
## prdline.my.fctriPadAir:.clusterid.fctr2
## -3.824864e+01
## prdline.my.fctriPadmini:.clusterid.fctr2
## 1.142155e+01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 3.398284e+01
## prdline.my.fctrUnknown:.clusterid.fctr3
## -4.613406e+00
## prdline.my.fctriPad 1:.clusterid.fctr3
## 1.277467e+01
## prdline.my.fctriPad 2:.clusterid.fctr3
## 1.452679e+01
## prdline.my.fctriPad 3+:.clusterid.fctr3
## 8.868811e+00
## prdline.my.fctriPadAir:.clusterid.fctr3
## -1.113572e+01
## prdline.my.fctriPadmini:.clusterid.fctr3
## -2.720336e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr3
## -7.412104e+00
## prdline.my.fctriPad 1:.clusterid.fctr4
## 2.954268e+01
## prdline.my.fctriPad 2:.clusterid.fctr4
## -2.510686e+01
## prdline.my.fctriPad 3+:.clusterid.fctr4
## 1.131957e+01
## prdline.my.fctriPadAir:.clusterid.fctr4
## -1.209120e+01
## prdline.my.fctriPadmini:.clusterid.fctr4
## -5.445225e+00
## prdline.my.fctriPad 2:.clusterid.fctr5
## 1.254241e+01
## prdline.my.fctriPadmini:.clusterid.fctr5
## 4.135022e+01
## character(0)
## character(0)
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.X.glmnet glmnet
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 9 1.457 0.04
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.6647981 101.8988 0.5586711 115.1109 0.6414783
## min.RMSESD.fit max.RsquaredSD.fit
## 1 5.733788 0.04264401
## label step_major step_minor bgn end elapsed
## 5 fit.models_1_glmnet 5 0 127.358 130.689 3.331
## 6 fit.models_1_rpart 6 0 130.690 NA NA
## [1] "fitting model: All.X.no.rnorm.rpart"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.052 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 1859
##
## CP nsplit rel error
## 1 0.20823484 0 1.0000000
## 2 0.15083018 1 0.7917652
## 3 0.05203829 2 0.6409350
##
## Variable importance
## biddable
## 45
## prdline.my.fctriPadAir
## 33
## idseq.my
## 9
## prdline.my.fctriPadAir:.clusterid.fctr2
## 4
## prdline.my.fctriPadAir:.clusterid.fctr3
## 3
## prdline.my.fctriPadAir:.clusterid.fctr4
## 3
## condition.fctrFor parts or not working
## 2
## color.fctrGold
## 1
## prdline.my.fctriPad 1
## 1
## D.npnct03.log
## 1
##
## Node number 1: 1859 observations, complexity param=0.2082348
## mean=211.3404, MSE=29020.33
## left son=2 (837 obs) right son=3 (1022 obs)
## Primary splits:
## biddable < 0.5 to the right, improve=0.20823480, (0 missing)
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.19258840, (0 missing)
## condition.fctrNew < 0.5 to the left, improve=0.18855550, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.07643644, (0 missing)
## color.fctrGold < 0.5 to the left, improve=0.07315212, (0 missing)
## Surrogate splits:
## idseq.my < 869.5 to the left, agree=0.639, adj=0.197, (0 split)
## condition.fctrFor parts or not working < 0.5 to the right, agree=0.567, adj=0.038, (0 split)
## prdline.my.fctriPad 1 < 0.5 to the right, agree=0.556, adj=0.013, (0 split)
## D.npnct03.log < 0.3465736 to the right, agree=0.555, adj=0.012, (0 split)
## D.npnct16.log < 0.8958797 to the right, agree=0.552, adj=0.006, (0 split)
##
## Node number 2: 837 observations
## mean=125.4409, MSE=18411.58
##
## Node number 3: 1022 observations, complexity param=0.1508302
## mean=281.6905, MSE=26716.52
## left son=6 (810 obs) right son=7 (212 obs)
## Primary splits:
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.29801570, (0 missing)
## condition.fctrNew < 0.5 to the left, improve=0.17632990, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.11899300, (0 missing)
## color.fctrGold < 0.5 to the left, improve=0.09694246, (0 missing)
## condition.fctrFor parts or not working < 0.5 to the right, improve=0.07350855, (0 missing)
## Surrogate splits:
## prdline.my.fctriPadAir:.clusterid.fctr2 < 0.5 to the left, agree=0.819, adj=0.127, (0 split)
## prdline.my.fctriPadAir:.clusterid.fctr3 < 0.5 to the left, agree=0.809, adj=0.080, (0 split)
## prdline.my.fctriPadAir:.clusterid.fctr4 < 0.5 to the left, agree=0.809, adj=0.080, (0 split)
## color.fctrGold < 0.5 to the left, agree=0.800, adj=0.038, (0 split)
##
## Node number 6: 810 observations
## mean=236.0411, MSE=17881.31
##
## Node number 7: 212 observations
## mean=456.1057, MSE=22091.13
##
## n= 1859
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 1859 53948800 211.3404
## 2) biddable>=0.5 837 15410490 125.4409 *
## 3) biddable< 0.5 1022 27304290 281.6905
## 6) prdline.my.fctriPadAir< 0.5 810 14483860 236.0411 *
## 7) prdline.my.fctriPadAir>=0.5 212 4683319 456.1057 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.X.no.rnorm.rpart rpart
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 1.821 0.126
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.359065 133.2986 0.3209143 142.7899 0.3897786
## min.RMSESD.fit max.RsquaredSD.fit
## 1 2.725981 0.0302287
## label step_major step_minor bgn end elapsed
## 6 fit.models_1_rpart 6 0 130.690 134.755 4.065
## 7 fit.models_1_rf 7 0 134.756 NA NA
## [1] "fitting model: All.X.no.rnorm.rf"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr"## Loading required package: randomForest
## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
##
## The following object is masked from 'package:dplyr':
##
## combine
##
## The following object is masked from 'package:gdata':
##
## combine
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 45 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 1859 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 1859 -none- numeric
## importance 89 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 1859 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 89 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.X.no.rnorm.rf rf
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 68.482 24.206
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.9238644 97.56147 0.5474904 116.5649 0.6705374
## min.RMSESD.fit max.RsquaredSD.fit
## 1 8.181706 0.0568418
## label step_major step_minor bgn end elapsed
## 7 fit.models_1_rf 7 0 134.756 206.054 71.298
## 8 fit.models_1_lm 8 0 206.054 NA NA
## [1] "fitting model: All.Interact.X.lm"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -389.35 -50.09 -3.80 45.59 754.10
##
## Coefficients: (10 not defined because of singularities)
## Estimate Std. Error
## (Intercept) -5.750e+04 5.618e+04
## `prdline.my.fctriPad 1` -2.883e+01 2.727e+01
## `prdline.my.fctriPad 2` 2.897e+01 2.692e+01
## `prdline.my.fctriPad 3+` 1.284e+02 2.583e+01
## prdline.my.fctriPadAir 2.559e+02 2.552e+01
## prdline.my.fctriPadmini 6.048e+01 2.667e+01
## `prdline.my.fctriPadmini 2+` 1.279e+02 2.834e+01
## `condition.fctrFor parts or not working` -9.841e+01 9.781e+00
## `condition.fctrManufacturer refurbished` 1.517e+01 1.652e+01
## condition.fctrNew 8.989e+01 8.025e+00
## `condition.fctrNew other (see details)` 6.111e+01 1.188e+01
## `condition.fctrSeller refurbished` -1.427e+01 1.045e+01
## color.fctrBlack -8.218e+00 6.580e+00
## color.fctrGold 5.163e+01 1.299e+01
## `color.fctrSpace Gray` 1.105e+01 8.518e+00
## color.fctrWhite 8.655e+00 6.327e+00
## D.TfIdf.sum.stem.stop.Ratio 4.864e+02 4.469e+02
## D.ratio.nstopwrds.nwrds 2.248e+02 1.610e+02
## `carrier.fctrAT&T` -5.989e+01 1.680e+01
## carrier.fctrOther -3.318e+01 6.085e+01
## carrier.fctrSprint -8.881e+01 2.357e+01
## `carrier.fctrT-Mobile` -5.977e+01 2.765e+01
## carrier.fctrUnknown -2.413e+01 1.202e+01
## carrier.fctrVerizon -5.797e+01 1.746e+01
## D.npnct09.log 7.868e+01 3.336e+01
## D.npnct10.log 3.622e+01 5.084e+01
## D.terms.n.stem.stop.Ratio 5.709e+04 5.616e+04
## D.npnct28.log -5.850e+01 4.954e+01
## cellular.fctr1 7.524e+01 1.510e+01
## cellular.fctrUnknown NA NA
## D.npnct14.log -1.420e+01 1.868e+01
## .rnorm 9.605e-01 2.342e+00
## D.npnct05.log -3.691e+01 3.362e+01
## D.npnct08.log -2.619e+01 1.737e+01
## D.npnct01.log 1.216e+01 1.512e+01
## D.ndgts.log 5.772e+00 1.193e+01
## D.npnct12.log -1.126e+01 1.611e+01
## D.npnct16.log 1.428e+01 4.917e+01
## D.npnct06.log -1.843e+01 5.342e+01
## D.npnct15.log -7.809e-01 2.939e+01
## D.npnct11.log -1.732e+01 8.993e+00
## D.npnct03.log -1.552e-01 4.030e+01
## storage.fctr16 -1.959e+02 1.178e+01
## storage.fctr32 -1.717e+02 1.267e+01
## storage.fctr64 -1.306e+02 1.230e+01
## storage.fctrUnknown -1.829e+02 1.717e+01
## D.npnct13.log -1.271e+01 9.086e+00
## D.terms.n.post.stem 4.132e+02 3.711e+02
## D.terms.n.post.stop -4.126e+02 3.703e+02
## D.ratio.sum.TfIdf.nwrds -1.328e+01 1.010e+01
## D.nstopwrds.log -7.958e+01 4.703e+01
## D.nwrds.unq.log -6.413e+04 6.279e+04
## D.terms.n.post.stem.log NA NA
## D.terms.n.post.stop.log 6.413e+04 6.278e+04
## D.nwrds.log 5.720e+01 6.615e+01
## D.nchrs.log 1.102e+02 6.788e+01
## D.nuppr.log -8.942e+01 5.100e+01
## D.npnct24.log 2.064e+01 1.321e+02
## D.TfIdf.sum.post.stem -7.044e+01 6.958e+01
## D.sum.TfIdf NA NA
## D.TfIdf.sum.post.stop 6.890e+01 6.677e+01
## biddable -4.916e+01 1.467e+01
## idseq.my 6.922e-02 1.485e-02
## `prdline.my.fctriPad 1:biddable` -6.944e+00 2.019e+01
## `prdline.my.fctriPad 2:biddable` -4.327e+01 1.919e+01
## `prdline.my.fctriPad 3+:biddable` -9.215e+01 1.887e+01
## `prdline.my.fctriPadAir:biddable` -1.529e+02 1.825e+01
## `prdline.my.fctriPadmini:biddable` -5.367e+01 1.883e+01
## `prdline.my.fctriPadmini 2+:biddable` -7.166e+01 2.053e+01
## `prdline.my.fctriPad 1:idseq.my` -4.357e-02 1.928e-02
## `prdline.my.fctriPad 2:idseq.my` -2.269e-02 1.840e-02
## `prdline.my.fctriPad 3+:idseq.my` -5.067e-02 1.777e-02
## `prdline.my.fctriPadAir:idseq.my` -6.376e-02 1.794e-02
## `prdline.my.fctriPadmini:idseq.my` -2.748e-02 1.833e-02
## `prdline.my.fctriPadmini 2+:idseq.my` -3.064e-02 2.057e-02
## `prdline.my.fctrUnknown:.clusterid.fctr2` 5.108e+01 1.920e+01
## `prdline.my.fctriPad 1:.clusterid.fctr2` -5.944e+00 2.220e+01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 2.498e+00 1.665e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 1.500e+01 1.828e+01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -3.079e+01 1.778e+01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.171e+01 2.130e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 3.451e+01 2.387e+01
## `prdline.my.fctrUnknown:.clusterid.fctr3` -3.537e+00 2.601e+01
## `prdline.my.fctriPad 1:.clusterid.fctr3` 9.372e+00 2.306e+01
## `prdline.my.fctriPad 2:.clusterid.fctr3` 2.486e+01 2.599e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 1.737e+00 1.935e+01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -1.546e+01 2.264e+01
## `prdline.my.fctriPadmini:.clusterid.fctr3` 8.329e-01 2.185e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -4.238e+00 2.586e+01
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 2.618e+01 2.512e+01
## `prdline.my.fctriPad 2:.clusterid.fctr4` -2.711e+01 2.745e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 5.477e+00 2.079e+01
## `prdline.my.fctriPadAir:.clusterid.fctr4` -2.158e+01 2.409e+01
## `prdline.my.fctriPadmini:.clusterid.fctr4` -9.082e+00 2.462e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 2.063e+01 3.022e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 3.881e+01 3.211e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## t value Pr(>|t|)
## (Intercept) -1.024 0.306180
## `prdline.my.fctriPad 1` -1.057 0.290545
## `prdline.my.fctriPad 2` 1.076 0.281984
## `prdline.my.fctriPad 3+` 4.973 7.23e-07 ***
## prdline.my.fctriPadAir 10.029 < 2e-16 ***
## prdline.my.fctriPadmini 2.268 0.023473 *
## `prdline.my.fctriPadmini 2+` 4.514 6.77e-06 ***
## `condition.fctrFor parts or not working` -10.061 < 2e-16 ***
## `condition.fctrManufacturer refurbished` 0.918 0.358624
## condition.fctrNew 11.201 < 2e-16 ***
## `condition.fctrNew other (see details)` 5.143 3.01e-07 ***
## `condition.fctrSeller refurbished` -1.365 0.172526
## color.fctrBlack -1.249 0.211840
## color.fctrGold 3.974 7.34e-05 ***
## `color.fctrSpace Gray` 1.298 0.194554
## color.fctrWhite 1.368 0.171557
## D.TfIdf.sum.stem.stop.Ratio 1.088 0.276572
## D.ratio.nstopwrds.nwrds 1.396 0.162988
## `carrier.fctrAT&T` -3.564 0.000375 ***
## carrier.fctrOther -0.545 0.585665
## carrier.fctrSprint -3.768 0.000170 ***
## `carrier.fctrT-Mobile` -2.162 0.030781 *
## carrier.fctrUnknown -2.008 0.044784 *
## carrier.fctrVerizon -3.320 0.000919 ***
## D.npnct09.log 2.359 0.018454 *
## D.npnct10.log 0.712 0.476274
## D.terms.n.stem.stop.Ratio 1.016 0.309550
## D.npnct28.log -1.181 0.237775
## cellular.fctr1 4.981 6.93e-07 ***
## cellular.fctrUnknown NA NA
## D.npnct14.log -0.760 0.447227
## .rnorm 0.410 0.681768
## D.npnct05.log -1.098 0.272395
## D.npnct08.log -1.508 0.131711
## D.npnct01.log 0.804 0.421497
## D.ndgts.log 0.484 0.628664
## D.npnct12.log -0.699 0.484770
## D.npnct16.log 0.290 0.771524
## D.npnct06.log -0.345 0.730137
## D.npnct15.log -0.027 0.978810
## D.npnct11.log -1.925 0.054332 .
## D.npnct03.log -0.004 0.996928
## storage.fctr16 -16.638 < 2e-16 ***
## storage.fctr32 -13.549 < 2e-16 ***
## storage.fctr64 -10.611 < 2e-16 ***
## storage.fctrUnknown -10.652 < 2e-16 ***
## D.npnct13.log -1.399 0.162128
## D.terms.n.post.stem 1.113 0.265680
## D.terms.n.post.stop -1.114 0.265372
## D.ratio.sum.TfIdf.nwrds -1.314 0.188888
## D.nstopwrds.log -1.692 0.090782 .
## D.nwrds.unq.log -1.021 0.307201
## D.terms.n.post.stem.log NA NA
## D.terms.n.post.stop.log 1.022 0.307112
## D.nwrds.log 0.865 0.387293
## D.nchrs.log 1.624 0.104554
## D.nuppr.log -1.753 0.079746 .
## D.npnct24.log 0.156 0.875904
## D.TfIdf.sum.post.stem -1.012 0.311475
## D.sum.TfIdf NA NA
## D.TfIdf.sum.post.stop 1.032 0.302236
## biddable -3.352 0.000820 ***
## idseq.my 4.662 3.36e-06 ***
## `prdline.my.fctriPad 1:biddable` -0.344 0.730956
## `prdline.my.fctriPad 2:biddable` -2.255 0.024249 *
## `prdline.my.fctriPad 3+:biddable` -4.882 1.14e-06 ***
## `prdline.my.fctriPadAir:biddable` -8.375 < 2e-16 ***
## `prdline.my.fctriPadmini:biddable` -2.850 0.004422 **
## `prdline.my.fctriPadmini 2+:biddable` -3.491 0.000493 ***
## `prdline.my.fctriPad 1:idseq.my` -2.260 0.023966 *
## `prdline.my.fctriPad 2:idseq.my` -1.233 0.217555
## `prdline.my.fctriPad 3+:idseq.my` -2.852 0.004400 **
## `prdline.my.fctriPadAir:idseq.my` -3.555 0.000388 ***
## `prdline.my.fctriPadmini:idseq.my` -1.499 0.133985
## `prdline.my.fctriPadmini 2+:idseq.my` -1.490 0.136475
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.661 0.007870 **
## `prdline.my.fctriPad 1:.clusterid.fctr2` -0.268 0.788864
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.150 0.880741
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.821 0.411864
## `prdline.my.fctriPadAir:.clusterid.fctr2` -1.731 0.083620 .
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.550 0.582519
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 1.445 0.148512
## `prdline.my.fctrUnknown:.clusterid.fctr3` -0.136 0.891837
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.406 0.684504
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.956 0.339027
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.090 0.928502
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.683 0.494814
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.038 0.969603
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.164 0.869864
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 1.042 0.297413
## `prdline.my.fctriPad 2:.clusterid.fctr4` -0.987 0.323534
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.263 0.792284
## `prdline.my.fctriPadAir:.clusterid.fctr4` -0.896 0.370383
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.369 0.712222
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.683 0.494936
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 1.209 0.226959
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 97.17 on 1766 degrees of freedom
## Multiple R-squared: 0.6909, Adjusted R-squared: 0.6748
## F-statistic: 42.91 on 92 and 1766 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading
## model_id model_method
## 1 All.Interact.X.lm lm
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.334 0.101
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.690917 99.41512 0.5271413 119.1518 0.6748153
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.6592594 5.921147 0.04305645
## label step_major step_minor bgn end elapsed
## 8 fit.models_1_lm 8 0 206.054 209.615 3.562
## 9 fit.models_1_glm 9 0 209.616 NA NA
## [1] "fitting model: All.Interact.X.glm"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -389.35 -50.09 -3.80 45.59 754.10
##
## Coefficients: (10 not defined because of singularities)
## Estimate Std. Error
## (Intercept) -5.750e+04 5.618e+04
## `prdline.my.fctriPad 1` -2.883e+01 2.727e+01
## `prdline.my.fctriPad 2` 2.897e+01 2.692e+01
## `prdline.my.fctriPad 3+` 1.284e+02 2.583e+01
## prdline.my.fctriPadAir 2.559e+02 2.552e+01
## prdline.my.fctriPadmini 6.048e+01 2.667e+01
## `prdline.my.fctriPadmini 2+` 1.279e+02 2.834e+01
## `condition.fctrFor parts or not working` -9.841e+01 9.781e+00
## `condition.fctrManufacturer refurbished` 1.517e+01 1.652e+01
## condition.fctrNew 8.989e+01 8.025e+00
## `condition.fctrNew other (see details)` 6.111e+01 1.188e+01
## `condition.fctrSeller refurbished` -1.427e+01 1.045e+01
## color.fctrBlack -8.218e+00 6.580e+00
## color.fctrGold 5.163e+01 1.299e+01
## `color.fctrSpace Gray` 1.105e+01 8.518e+00
## color.fctrWhite 8.655e+00 6.327e+00
## D.TfIdf.sum.stem.stop.Ratio 4.864e+02 4.469e+02
## D.ratio.nstopwrds.nwrds 2.248e+02 1.610e+02
## `carrier.fctrAT&T` -5.989e+01 1.680e+01
## carrier.fctrOther -3.318e+01 6.085e+01
## carrier.fctrSprint -8.881e+01 2.357e+01
## `carrier.fctrT-Mobile` -5.977e+01 2.765e+01
## carrier.fctrUnknown -2.413e+01 1.202e+01
## carrier.fctrVerizon -5.797e+01 1.746e+01
## D.npnct09.log 7.868e+01 3.336e+01
## D.npnct10.log 3.622e+01 5.084e+01
## D.terms.n.stem.stop.Ratio 5.709e+04 5.616e+04
## D.npnct28.log -5.850e+01 4.954e+01
## cellular.fctr1 7.524e+01 1.510e+01
## cellular.fctrUnknown NA NA
## D.npnct14.log -1.420e+01 1.868e+01
## .rnorm 9.605e-01 2.342e+00
## D.npnct05.log -3.691e+01 3.362e+01
## D.npnct08.log -2.619e+01 1.737e+01
## D.npnct01.log 1.216e+01 1.512e+01
## D.ndgts.log 5.772e+00 1.193e+01
## D.npnct12.log -1.126e+01 1.611e+01
## D.npnct16.log 1.428e+01 4.917e+01
## D.npnct06.log -1.843e+01 5.342e+01
## D.npnct15.log -7.809e-01 2.939e+01
## D.npnct11.log -1.732e+01 8.993e+00
## D.npnct03.log -1.552e-01 4.030e+01
## storage.fctr16 -1.959e+02 1.178e+01
## storage.fctr32 -1.717e+02 1.267e+01
## storage.fctr64 -1.306e+02 1.230e+01
## storage.fctrUnknown -1.829e+02 1.717e+01
## D.npnct13.log -1.271e+01 9.086e+00
## D.terms.n.post.stem 4.132e+02 3.711e+02
## D.terms.n.post.stop -4.126e+02 3.703e+02
## D.ratio.sum.TfIdf.nwrds -1.328e+01 1.010e+01
## D.nstopwrds.log -7.958e+01 4.703e+01
## D.nwrds.unq.log -6.413e+04 6.279e+04
## D.terms.n.post.stem.log NA NA
## D.terms.n.post.stop.log 6.413e+04 6.278e+04
## D.nwrds.log 5.720e+01 6.615e+01
## D.nchrs.log 1.102e+02 6.788e+01
## D.nuppr.log -8.942e+01 5.100e+01
## D.npnct24.log 2.064e+01 1.321e+02
## D.TfIdf.sum.post.stem -7.044e+01 6.958e+01
## D.sum.TfIdf NA NA
## D.TfIdf.sum.post.stop 6.890e+01 6.677e+01
## biddable -4.916e+01 1.467e+01
## idseq.my 6.922e-02 1.485e-02
## `prdline.my.fctriPad 1:biddable` -6.944e+00 2.019e+01
## `prdline.my.fctriPad 2:biddable` -4.327e+01 1.919e+01
## `prdline.my.fctriPad 3+:biddable` -9.215e+01 1.887e+01
## `prdline.my.fctriPadAir:biddable` -1.529e+02 1.825e+01
## `prdline.my.fctriPadmini:biddable` -5.367e+01 1.883e+01
## `prdline.my.fctriPadmini 2+:biddable` -7.166e+01 2.053e+01
## `prdline.my.fctriPad 1:idseq.my` -4.357e-02 1.928e-02
## `prdline.my.fctriPad 2:idseq.my` -2.269e-02 1.840e-02
## `prdline.my.fctriPad 3+:idseq.my` -5.067e-02 1.777e-02
## `prdline.my.fctriPadAir:idseq.my` -6.376e-02 1.794e-02
## `prdline.my.fctriPadmini:idseq.my` -2.748e-02 1.833e-02
## `prdline.my.fctriPadmini 2+:idseq.my` -3.064e-02 2.057e-02
## `prdline.my.fctrUnknown:.clusterid.fctr2` 5.108e+01 1.920e+01
## `prdline.my.fctriPad 1:.clusterid.fctr2` -5.944e+00 2.220e+01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 2.498e+00 1.665e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 1.500e+01 1.828e+01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -3.079e+01 1.778e+01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.171e+01 2.130e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 3.451e+01 2.387e+01
## `prdline.my.fctrUnknown:.clusterid.fctr3` -3.537e+00 2.601e+01
## `prdline.my.fctriPad 1:.clusterid.fctr3` 9.372e+00 2.306e+01
## `prdline.my.fctriPad 2:.clusterid.fctr3` 2.486e+01 2.599e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 1.737e+00 1.935e+01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -1.546e+01 2.264e+01
## `prdline.my.fctriPadmini:.clusterid.fctr3` 8.329e-01 2.185e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -4.238e+00 2.586e+01
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 2.618e+01 2.512e+01
## `prdline.my.fctriPad 2:.clusterid.fctr4` -2.711e+01 2.745e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 5.477e+00 2.079e+01
## `prdline.my.fctriPadAir:.clusterid.fctr4` -2.158e+01 2.409e+01
## `prdline.my.fctriPadmini:.clusterid.fctr4` -9.082e+00 2.462e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 2.063e+01 3.022e+01
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 3.881e+01 3.211e+01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## t value Pr(>|t|)
## (Intercept) -1.024 0.306180
## `prdline.my.fctriPad 1` -1.057 0.290545
## `prdline.my.fctriPad 2` 1.076 0.281984
## `prdline.my.fctriPad 3+` 4.973 7.23e-07 ***
## prdline.my.fctriPadAir 10.029 < 2e-16 ***
## prdline.my.fctriPadmini 2.268 0.023473 *
## `prdline.my.fctriPadmini 2+` 4.514 6.77e-06 ***
## `condition.fctrFor parts or not working` -10.061 < 2e-16 ***
## `condition.fctrManufacturer refurbished` 0.918 0.358624
## condition.fctrNew 11.201 < 2e-16 ***
## `condition.fctrNew other (see details)` 5.143 3.01e-07 ***
## `condition.fctrSeller refurbished` -1.365 0.172526
## color.fctrBlack -1.249 0.211840
## color.fctrGold 3.974 7.34e-05 ***
## `color.fctrSpace Gray` 1.298 0.194554
## color.fctrWhite 1.368 0.171557
## D.TfIdf.sum.stem.stop.Ratio 1.088 0.276572
## D.ratio.nstopwrds.nwrds 1.396 0.162988
## `carrier.fctrAT&T` -3.564 0.000375 ***
## carrier.fctrOther -0.545 0.585665
## carrier.fctrSprint -3.768 0.000170 ***
## `carrier.fctrT-Mobile` -2.162 0.030781 *
## carrier.fctrUnknown -2.008 0.044784 *
## carrier.fctrVerizon -3.320 0.000919 ***
## D.npnct09.log 2.359 0.018454 *
## D.npnct10.log 0.712 0.476274
## D.terms.n.stem.stop.Ratio 1.016 0.309550
## D.npnct28.log -1.181 0.237775
## cellular.fctr1 4.981 6.93e-07 ***
## cellular.fctrUnknown NA NA
## D.npnct14.log -0.760 0.447227
## .rnorm 0.410 0.681768
## D.npnct05.log -1.098 0.272395
## D.npnct08.log -1.508 0.131711
## D.npnct01.log 0.804 0.421497
## D.ndgts.log 0.484 0.628664
## D.npnct12.log -0.699 0.484770
## D.npnct16.log 0.290 0.771524
## D.npnct06.log -0.345 0.730137
## D.npnct15.log -0.027 0.978810
## D.npnct11.log -1.925 0.054332 .
## D.npnct03.log -0.004 0.996928
## storage.fctr16 -16.638 < 2e-16 ***
## storage.fctr32 -13.549 < 2e-16 ***
## storage.fctr64 -10.611 < 2e-16 ***
## storage.fctrUnknown -10.652 < 2e-16 ***
## D.npnct13.log -1.399 0.162128
## D.terms.n.post.stem 1.113 0.265680
## D.terms.n.post.stop -1.114 0.265372
## D.ratio.sum.TfIdf.nwrds -1.314 0.188888
## D.nstopwrds.log -1.692 0.090782 .
## D.nwrds.unq.log -1.021 0.307201
## D.terms.n.post.stem.log NA NA
## D.terms.n.post.stop.log 1.022 0.307112
## D.nwrds.log 0.865 0.387293
## D.nchrs.log 1.624 0.104554
## D.nuppr.log -1.753 0.079746 .
## D.npnct24.log 0.156 0.875904
## D.TfIdf.sum.post.stem -1.012 0.311475
## D.sum.TfIdf NA NA
## D.TfIdf.sum.post.stop 1.032 0.302236
## biddable -3.352 0.000820 ***
## idseq.my 4.662 3.36e-06 ***
## `prdline.my.fctriPad 1:biddable` -0.344 0.730956
## `prdline.my.fctriPad 2:biddable` -2.255 0.024249 *
## `prdline.my.fctriPad 3+:biddable` -4.882 1.14e-06 ***
## `prdline.my.fctriPadAir:biddable` -8.375 < 2e-16 ***
## `prdline.my.fctriPadmini:biddable` -2.850 0.004422 **
## `prdline.my.fctriPadmini 2+:biddable` -3.491 0.000493 ***
## `prdline.my.fctriPad 1:idseq.my` -2.260 0.023966 *
## `prdline.my.fctriPad 2:idseq.my` -1.233 0.217555
## `prdline.my.fctriPad 3+:idseq.my` -2.852 0.004400 **
## `prdline.my.fctriPadAir:idseq.my` -3.555 0.000388 ***
## `prdline.my.fctriPadmini:idseq.my` -1.499 0.133985
## `prdline.my.fctriPadmini 2+:idseq.my` -1.490 0.136475
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.661 0.007870 **
## `prdline.my.fctriPad 1:.clusterid.fctr2` -0.268 0.788864
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.150 0.880741
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.821 0.411864
## `prdline.my.fctriPadAir:.clusterid.fctr2` -1.731 0.083620 .
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.550 0.582519
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 1.445 0.148512
## `prdline.my.fctrUnknown:.clusterid.fctr3` -0.136 0.891837
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.406 0.684504
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.956 0.339027
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.090 0.928502
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.683 0.494814
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.038 0.969603
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.164 0.869864
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 1.042 0.297413
## `prdline.my.fctriPad 2:.clusterid.fctr4` -0.987 0.323534
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.263 0.792284
## `prdline.my.fctriPadAir:.clusterid.fctr4` -0.896 0.370383
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.369 0.712222
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.683 0.494936
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 1.209 0.226959
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 9442.047)
##
## Null deviance: 53948796 on 1858 degrees of freedom
## Residual deviance: 16674654 on 1766 degrees of freedom
## AIC: 22383
##
## Number of Fisher Scoring iterations: 2
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## model_id model_method
## 1 All.Interact.X.glm glm
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.298 0.116
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB min.aic.fit
## 1 0.690917 99.41512 0.5271413 119.1518 22383.5
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.6592594 5.921147 0.04305645
## label step_major step_minor bgn end elapsed
## 9 fit.models_1_glm 9 0 209.616 213.176 3.56
## 10 fit.models_1_bayesglm 10 0 213.176 NA NA
## [1] "fitting model: All.Interact.X.bayesglm"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -389.46 -49.53 -3.85 46.07 753.88
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -139.52218 686.99171
## `prdline.my.fctriPad 1` -29.26925 27.25138
## `prdline.my.fctriPad 2` 28.13115 26.90806
## `prdline.my.fctriPad 3+` 128.02473 25.78128
## prdline.my.fctriPadAir 255.40708 25.50005
## prdline.my.fctriPadmini 60.50207 26.64229
## `prdline.my.fctriPadmini 2+` 127.45212 28.33410
## `condition.fctrFor parts or not working` -98.53473 9.80899
## `condition.fctrManufacturer refurbished` 15.61754 16.55305
## condition.fctrNew 89.83256 8.04847
## `condition.fctrNew other (see details)` 60.65891 11.90632
## `condition.fctrSeller refurbished` -14.63050 10.46508
## color.fctrBlack -8.07146 6.59790
## color.fctrGold 51.74234 13.02680
## `color.fctrSpace Gray` 11.13454 8.53854
## color.fctrWhite 8.44733 6.33197
## D.TfIdf.sum.stem.stop.Ratio 336.05678 375.53554
## D.ratio.nstopwrds.nwrds 206.62382 154.76495
## `carrier.fctrAT&T` -9.74255 221.18345
## carrier.fctrOther 15.23530 226.89073
## carrier.fctrSprint -38.27509 221.65345
## `carrier.fctrT-Mobile` -10.02371 221.98830
## carrier.fctrUnknown 26.10599 221.19253
## carrier.fctrVerizon -8.47360 221.22550
## D.npnct09.log 78.47889 33.33090
## D.npnct10.log 36.65482 50.80637
## D.terms.n.stem.stop.Ratio -105.68544 560.98511
## D.npnct28.log -58.35565 49.45195
## cellular.fctr1 25.02216 221.10731
## cellular.fctrUnknown -50.33495 221.43759
## D.npnct14.log -13.44312 18.66538
## .rnorm 0.93571 2.34908
## D.npnct05.log -34.84764 33.52346
## D.npnct08.log -25.68151 17.40369
## D.npnct01.log 11.88016 15.14965
## D.ndgts.log 5.73620 11.90104
## D.npnct12.log -9.91423 16.11164
## D.npnct16.log 13.64798 48.98091
## D.npnct06.log -19.63710 53.19248
## D.npnct15.log -1.53502 29.42054
## D.npnct11.log -18.14638 8.97726
## D.npnct03.log 2.05570 40.23739
## storage.fctr16 -195.09787 11.79293
## storage.fctr32 -170.65532 12.68538
## storage.fctr64 -129.80484 12.32648
## storage.fctrUnknown -181.89089 17.19827
## D.npnct13.log -13.13244 9.07922
## D.terms.n.post.stem 31.33054 62.07114
## D.terms.n.post.stop -31.09525 61.39910
## D.ratio.sum.TfIdf.nwrds -13.62301 9.99677
## D.nstopwrds.log -71.88221 45.46350
## D.nwrds.unq.log -31.48629 578.11071
## D.terms.n.post.stem.log -31.48629 578.11071
## D.terms.n.post.stop.log 67.91273 521.12639
## D.nwrds.log 49.24496 64.96154
## D.nchrs.log 108.64199 67.24722
## D.nuppr.log -87.50763 50.63015
## D.npnct24.log 7.37711 127.62014
## D.TfIdf.sum.post.stem -23.31191 494.92411
## D.sum.TfIdf -23.31191 494.92411
## D.TfIdf.sum.post.stop 46.01392 56.43503
## biddable -49.37561 14.66661
## idseq.my 0.06920 0.01485
## `prdline.my.fctriPad 1:biddable` -6.70148 20.21308
## `prdline.my.fctriPad 2:biddable` -43.26706 19.20071
## `prdline.my.fctriPad 3+:biddable` -91.44584 18.87420
## `prdline.my.fctriPadAir:biddable` -152.13016 18.26799
## `prdline.my.fctriPadmini:biddable` -53.50452 18.84445
## `prdline.my.fctriPadmini 2+:biddable` -71.13292 20.54804
## `prdline.my.fctriPad 1:idseq.my` -0.04356 0.01930
## `prdline.my.fctriPad 2:idseq.my` -0.02196 0.01841
## `prdline.my.fctriPad 3+:idseq.my` -0.05077 0.01777
## `prdline.my.fctriPadAir:idseq.my` -0.06366 0.01795
## `prdline.my.fctriPadmini:idseq.my` -0.02773 0.01833
## `prdline.my.fctriPadmini 2+:idseq.my` -0.03038 0.02060
## `prdline.my.fctrUnknown:.clusterid.fctr2` 51.22284 19.21776
## `prdline.my.fctriPad 1:.clusterid.fctr2` -5.36491 22.23051
## `prdline.my.fctriPad 2:.clusterid.fctr2` 2.76536 16.64877
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 15.62933 18.30413
## `prdline.my.fctriPadAir:.clusterid.fctr2` -30.90206 17.81348
## `prdline.my.fctriPadmini:.clusterid.fctr2` 11.12455 21.32900
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 35.90846 23.83945
## `prdline.my.fctrUnknown:.clusterid.fctr3` -3.57270 26.03858
## `prdline.my.fctriPad 1:.clusterid.fctr3` 10.89063 23.05523
## `prdline.my.fctriPad 2:.clusterid.fctr3` 23.66675 25.98107
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 2.82453 19.35074
## `prdline.my.fctriPadAir:.clusterid.fctr3` -13.56764 22.62011
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.52071 21.89617
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -5.03653 25.89675
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.00000 854.89301
## `prdline.my.fctriPad 1:.clusterid.fctr4` 26.66759 25.15931
## `prdline.my.fctriPad 2:.clusterid.fctr4` -26.24886 27.46768
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 6.77872 20.81011
## `prdline.my.fctriPadAir:.clusterid.fctr4` -19.99073 24.06761
## `prdline.my.fctriPadmini:.clusterid.fctr4` -8.33590 24.63992
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.00000 854.89301
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.00000 854.89301
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.00000 854.89301
## `prdline.my.fctriPad 2:.clusterid.fctr5` 21.09303 30.23976
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.00000 854.89301
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.00000 854.89301
## `prdline.my.fctriPadmini:.clusterid.fctr5` 40.51999 32.11356
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.00000 854.89301
## t value Pr(>|t|)
## (Intercept) -0.203 0.839087
## `prdline.my.fctriPad 1` -1.074 0.282949
## `prdline.my.fctriPad 2` 1.045 0.295957
## `prdline.my.fctriPad 3+` 4.966 7.51e-07 ***
## prdline.my.fctriPadAir 10.016 < 2e-16 ***
## prdline.my.fctriPadmini 2.271 0.023273 *
## `prdline.my.fctriPadmini 2+` 4.498 7.30e-06 ***
## `condition.fctrFor parts or not working` -10.045 < 2e-16 ***
## `condition.fctrManufacturer refurbished` 0.943 0.345563
## condition.fctrNew 11.161 < 2e-16 ***
## `condition.fctrNew other (see details)` 5.095 3.87e-07 ***
## `condition.fctrSeller refurbished` -1.398 0.162280
## color.fctrBlack -1.223 0.221366
## color.fctrGold 3.972 7.42e-05 ***
## `color.fctrSpace Gray` 1.304 0.192393
## color.fctrWhite 1.334 0.182352
## D.TfIdf.sum.stem.stop.Ratio 0.895 0.370977
## D.ratio.nstopwrds.nwrds 1.335 0.182023
## `carrier.fctrAT&T` -0.044 0.964872
## carrier.fctrOther 0.067 0.946471
## carrier.fctrSprint -0.173 0.862923
## `carrier.fctrT-Mobile` -0.045 0.963990
## carrier.fctrUnknown 0.118 0.906062
## carrier.fctrVerizon -0.038 0.969450
## D.npnct09.log 2.355 0.018655 *
## D.npnct10.log 0.721 0.470722
## D.terms.n.stem.stop.Ratio -0.188 0.850591
## D.npnct28.log -1.180 0.238141
## cellular.fctr1 0.113 0.909911
## cellular.fctrUnknown -0.227 0.820209
## D.npnct14.log -0.720 0.471488
## .rnorm 0.398 0.690437
## D.npnct05.log -1.039 0.298715
## D.npnct08.log -1.476 0.140221
## D.npnct01.log 0.784 0.433036
## D.ndgts.log 0.482 0.629872
## D.npnct12.log -0.615 0.538406
## D.npnct16.log 0.279 0.780555
## D.npnct06.log -0.369 0.712045
## D.npnct15.log -0.052 0.958395
## D.npnct11.log -2.021 0.043393 *
## D.npnct03.log 0.051 0.959260
## storage.fctr16 -16.544 < 2e-16 ***
## storage.fctr32 -13.453 < 2e-16 ***
## storage.fctr64 -10.531 < 2e-16 ***
## storage.fctrUnknown -10.576 < 2e-16 ***
## D.npnct13.log -1.446 0.148236
## D.terms.n.post.stem 0.505 0.613796
## D.terms.n.post.stop -0.506 0.612608
## D.ratio.sum.TfIdf.nwrds -1.363 0.173139
## D.nstopwrds.log -1.581 0.114036
## D.nwrds.unq.log -0.054 0.956572
## D.terms.n.post.stem.log -0.054 0.956572
## D.terms.n.post.stop.log 0.130 0.896329
## D.nwrds.log 0.758 0.448515
## D.nchrs.log 1.616 0.106369
## D.nuppr.log -1.728 0.084098 .
## D.npnct24.log 0.058 0.953910
## D.TfIdf.sum.post.stem -0.047 0.962437
## D.sum.TfIdf -0.047 0.962437
## D.TfIdf.sum.post.stop 0.815 0.414986
## biddable -3.367 0.000778 ***
## idseq.my 4.660 3.39e-06 ***
## `prdline.my.fctriPad 1:biddable` -0.332 0.740275
## `prdline.my.fctriPad 2:biddable` -2.253 0.024356 *
## `prdline.my.fctriPad 3+:biddable` -4.845 1.38e-06 ***
## `prdline.my.fctriPadAir:biddable` -8.328 < 2e-16 ***
## `prdline.my.fctriPadmini:biddable` -2.839 0.004574 **
## `prdline.my.fctriPadmini 2+:biddable` -3.462 0.000549 ***
## `prdline.my.fctriPad 1:idseq.my` -2.257 0.024119 *
## `prdline.my.fctriPad 2:idseq.my` -1.192 0.233286
## `prdline.my.fctriPad 3+:idseq.my` -2.856 0.004336 **
## `prdline.my.fctriPadAir:idseq.my` -3.546 0.000401 ***
## `prdline.my.fctriPadmini:idseq.my` -1.512 0.130619
## `prdline.my.fctriPadmini 2+:idseq.my` -1.475 0.140328
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.665 0.007760 **
## `prdline.my.fctriPad 1:.clusterid.fctr2` -0.241 0.809327
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.166 0.868097
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.854 0.393294
## `prdline.my.fctriPadAir:.clusterid.fctr2` -1.735 0.082959 .
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.522 0.602036
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 1.506 0.132180
## `prdline.my.fctrUnknown:.clusterid.fctr3` -0.137 0.890882
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.472 0.636720
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.911 0.362461
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.146 0.883966
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.600 0.548714
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.024 0.981030
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.194 0.845819
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.000 1.000000
## `prdline.my.fctriPad 1:.clusterid.fctr4` 1.060 0.289314
## `prdline.my.fctriPad 2:.clusterid.fctr4` -0.956 0.339392
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.326 0.744659
## `prdline.my.fctriPadAir:.clusterid.fctr4` -0.831 0.406308
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.338 0.735171
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.000 1.000000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.698 0.485566
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPadmini:.clusterid.fctr5` 1.262 0.207199
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.000 1.000000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 9502.165)
##
## Null deviance: 53948796 on 1858 degrees of freedom
## Residual deviance: 16685801 on 1756 degrees of freedom
## AIC: 22405
##
## Number of Fisher Scoring iterations: 12
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.Interact.X.bayesglm bayesglm
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 2.999 0.982
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB min.aic.fit
## 1 0.6907104 99.3622 0.5274745 119.1099 22404.74
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.6595932 5.855718 0.04250045
## label step_major step_minor bgn end elapsed
## 10 fit.models_1_bayesglm 10 0 213.176 217.458 4.282
## 11 fit.models_1_glmnet 11 0 217.459 NA NA
## [1] "fitting model: All.Interact.X.glmnet"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.155 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: alpha## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: lambda
## Length Class Mode
## a0 92 -none- numeric
## beta 9384 dgCMatrix S4
## df 92 -none- numeric
## dim 2 -none- numeric
## lambda 92 -none- numeric
## dev.ratio 92 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 102 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 2.021265e+02
## prdline.my.fctriPad 1
## -6.494437e+01
## prdline.my.fctriPad 2
## -6.483523e+00
## prdline.my.fctriPad 3+
## 8.594133e+01
## prdline.my.fctriPadAir
## 2.134242e+02
## prdline.my.fctriPadmini
## 1.815435e+01
## prdline.my.fctriPadmini 2+
## 8.421616e+01
## condition.fctrFor parts or not working
## -9.684398e+01
## condition.fctrManufacturer refurbished
## 1.532624e+01
## condition.fctrNew
## 9.001168e+01
## condition.fctrNew other (see details)
## 6.017169e+01
## condition.fctrSeller refurbished
## -1.371126e+01
## color.fctrBlack
## -8.568007e+00
## color.fctrGold
## 4.977860e+01
## color.fctrSpace Gray
## 1.144084e+01
## color.fctrWhite
## 8.573792e+00
## D.TfIdf.sum.stem.stop.Ratio
## 3.415501e+01
## D.ratio.nstopwrds.nwrds
## 9.430589e-02
## carrier.fctrAT&T
## -5.891599e-01
## carrier.fctrOther
## 3.080510e+00
## carrier.fctrSprint
## -2.591294e+01
## carrier.fctrUnknown
## 3.485803e+01
## D.npnct09.log
## 8.804685e+01
## D.npnct10.log
## 3.950591e+01
## D.terms.n.stem.stop.Ratio
## 9.150614e+01
## D.npnct28.log
## -5.938833e+01
## cellular.fctr1
## 1.585526e+01
## cellular.fctrUnknown
## -5.810206e+01
## D.npnct14.log
## -1.047234e+01
## .rnorm
## 5.698951e-01
## D.npnct05.log
## -3.097552e+01
## D.npnct08.log
## -1.878605e+01
## D.npnct01.log
## 1.327287e+01
## D.ndgts.log
## 9.023380e+00
## D.npnct06.log
## -3.296546e+00
## D.npnct15.log
## -1.185069e-01
## D.npnct11.log
## -1.419400e+01
## D.npnct03.log
## 1.000567e+01
## storage.fctr16
## -1.877873e+02
## storage.fctr32
## -1.629746e+02
## storage.fctr64
## -1.227080e+02
## storage.fctrUnknown
## -1.758608e+02
## D.npnct13.log
## -9.116810e+00
## D.terms.n.post.stem
## 1.026634e-01
## D.ratio.sum.TfIdf.nwrds
## -1.364773e+01
## D.nstopwrds.log
## 1.508679e+00
## D.nwrds.log
## 3.854197e-01
## D.TfIdf.sum.post.stem
## 6.677352e-04
## D.sum.TfIdf
## 1.517063e-16
## D.TfIdf.sum.post.stop
## 1.002609e-01
## biddable
## -6.179469e+01
## idseq.my
## 4.426736e-02
## prdline.my.fctriPad 1:biddable
## 2.952360e+00
## prdline.my.fctriPad 2:biddable
## -3.166519e+01
## prdline.my.fctriPad 3+:biddable
## -7.568148e+01
## prdline.my.fctriPadAir:biddable
## -1.377437e+02
## prdline.my.fctriPadmini:biddable
## -3.893092e+01
## prdline.my.fctriPadmini 2+:biddable
## -5.638895e+01
## prdline.my.fctriPad 1:idseq.my
## -1.754718e-02
## prdline.my.fctriPad 3+:idseq.my
## -2.327996e-02
## prdline.my.fctriPadAir:idseq.my
## -3.540788e-02
## prdline.my.fctriPadmini 2+:idseq.my
## -4.520830e-04
## prdline.my.fctrUnknown:.clusterid.fctr2
## 4.414803e+01
## prdline.my.fctriPad 1:.clusterid.fctr2
## -7.083480e+00
## prdline.my.fctriPad 3+:.clusterid.fctr2
## 1.550777e+01
## prdline.my.fctriPadAir:.clusterid.fctr2
## -2.923902e+01
## prdline.my.fctriPadmini:.clusterid.fctr2
## 9.439323e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 3.474124e+01
## prdline.my.fctrUnknown:.clusterid.fctr3
## -8.725457e+00
## prdline.my.fctriPad 1:.clusterid.fctr3
## 7.339137e+00
## prdline.my.fctriPad 2:.clusterid.fctr3
## 1.368954e+01
## prdline.my.fctriPad 3+:.clusterid.fctr3
## 2.146605e+00
## prdline.my.fctriPadAir:.clusterid.fctr3
## -8.634329e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr3
## -3.851540e+00
## prdline.my.fctriPad 1:.clusterid.fctr4
## 2.206077e+01
## prdline.my.fctriPad 2:.clusterid.fctr4
## -2.588906e+01
## prdline.my.fctriPad 3+:.clusterid.fctr4
## 7.506176e+00
## prdline.my.fctriPadAir:.clusterid.fctr4
## -1.613339e+01
## prdline.my.fctriPadmini:.clusterid.fctr4
## -5.690045e+00
## prdline.my.fctriPad 2:.clusterid.fctr5
## 1.264092e+01
## prdline.my.fctriPadmini:.clusterid.fctr5
## 3.774607e+01
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 8.475655e+01
## prdline.my.fctriPad 1
## -3.659582e+01
## prdline.my.fctriPad 2
## 2.021166e+01
## prdline.my.fctriPad 3+
## 1.197013e+02
## prdline.my.fctriPadAir
## 2.474283e+02
## prdline.my.fctriPadmini
## 5.258541e+01
## prdline.my.fctriPadmini 2+
## 1.199575e+02
## condition.fctrFor parts or not working
## -9.879889e+01
## condition.fctrManufacturer refurbished
## 1.596868e+01
## condition.fctrNew
## 8.952577e+01
## condition.fctrNew other (see details)
## 5.971614e+01
## condition.fctrSeller refurbished
## -1.472606e+01
## color.fctrBlack
## -7.864750e+00
## color.fctrGold
## 5.153175e+01
## color.fctrSpace Gray
## 1.217576e+01
## color.fctrWhite
## 9.339784e+00
## D.TfIdf.sum.stem.stop.Ratio
## 4.619262e+01
## D.ratio.nstopwrds.nwrds
## 6.084916e+01
## carrier.fctrAT&T
## -8.563862e-01
## carrier.fctrOther
## 1.208200e+01
## carrier.fctrSprint
## -2.898136e+01
## carrier.fctrT-Mobile
## -1.712969e+00
## carrier.fctrUnknown
## 3.553437e+01
## carrier.fctrVerizon
## 3.864579e-02
## D.npnct09.log
## 9.122460e+01
## D.npnct10.log
## 4.397425e+01
## D.terms.n.stem.stop.Ratio
## 1.109711e+02
## D.npnct28.log
## -6.566439e+01
## cellular.fctr1
## 1.603608e+01
## cellular.fctrUnknown
## -5.965857e+01
## D.npnct14.log
## -1.133446e+01
## .rnorm
## 7.961739e-01
## D.npnct05.log
## -2.773098e+01
## D.npnct08.log
## -2.191823e+01
## D.npnct01.log
## 1.380050e+01
## D.ndgts.log
## 7.746915e+00
## D.npnct12.log
## -3.505011e+00
## D.npnct16.log
## 1.305669e+01
## D.npnct06.log
## -1.853178e+01
## D.npnct15.log
## -1.643859e+00
## D.npnct11.log
## -1.584157e+01
## D.npnct03.log
## 1.180179e+01
## storage.fctr16
## -1.931560e+02
## storage.fctr32
## -1.685737e+02
## storage.fctr64
## -1.282895e+02
## storage.fctrUnknown
## -1.805276e+02
## D.npnct13.log
## -1.069285e+01
## D.terms.n.post.stem
## -2.233931e-04
## D.terms.n.post.stop
## -1.626424e-01
## D.ratio.sum.TfIdf.nwrds
## -1.239938e+01
## D.nstopwrds.log
## -2.509072e+01
## D.nwrds.unq.log
## 5.336434e-03
## D.nwrds.log
## 3.848297e+01
## D.nchrs.log
## 1.637521e+00
## D.nuppr.log
## -7.563751e+00
## D.npnct24.log
## 9.285074e-02
## D.TfIdf.sum.post.stop
## 1.360046e+00
## biddable
## -5.199430e+01
## idseq.my
## 6.499436e-02
## prdline.my.fctriPad 1:biddable
## -4.561164e+00
## prdline.my.fctriPad 2:biddable
## -4.067152e+01
## prdline.my.fctriPad 3+:biddable
## -8.845897e+01
## prdline.my.fctriPadAir:biddable
## -1.491058e+02
## prdline.my.fctriPadmini:biddable
## -5.072429e+01
## prdline.my.fctriPadmini 2+:biddable
## -6.833458e+01
## prdline.my.fctriPad 1:idseq.my
## -3.831010e-02
## prdline.my.fctriPad 2:idseq.my
## -1.785500e-02
## prdline.my.fctriPad 3+:idseq.my
## -4.599684e-02
## prdline.my.fctriPadAir:idseq.my
## -5.895835e-02
## prdline.my.fctriPadmini:idseq.my
## -2.311126e-02
## prdline.my.fctriPadmini 2+:idseq.my
## -2.594274e-02
## prdline.my.fctrUnknown:.clusterid.fctr2
## 4.974547e+01
## prdline.my.fctriPad 1:.clusterid.fctr2
## -5.252069e+00
## prdline.my.fctriPad 2:.clusterid.fctr2
## 3.080331e+00
## prdline.my.fctriPad 3+:.clusterid.fctr2
## 1.595769e+01
## prdline.my.fctriPadAir:.clusterid.fctr2
## -3.024417e+01
## prdline.my.fctriPadmini:.clusterid.fctr2
## 1.177981e+01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 3.571423e+01
## prdline.my.fctrUnknown:.clusterid.fctr3
## -3.989763e+00
## prdline.my.fctriPad 1:.clusterid.fctr3
## 1.018699e+01
## prdline.my.fctriPad 2:.clusterid.fctr3
## 1.961897e+01
## prdline.my.fctriPad 3+:.clusterid.fctr3
## 3.708763e+00
## prdline.my.fctriPadAir:.clusterid.fctr3
## -1.143213e+01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3
## -3.830877e+00
## prdline.my.fctriPad 1:.clusterid.fctr4
## 2.628033e+01
## prdline.my.fctriPad 2:.clusterid.fctr4
## -2.388832e+01
## prdline.my.fctriPad 3+:.clusterid.fctr4
## 8.789285e+00
## prdline.my.fctriPadAir:.clusterid.fctr4
## -2.006124e+01
## prdline.my.fctriPadmini:.clusterid.fctr4
## -7.795470e+00
## prdline.my.fctriPad 2:.clusterid.fctr5
## 1.970114e+01
## prdline.my.fctriPadmini:.clusterid.fctr5
## 4.169729e+01
## character(0)
## character(0)
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.Interact.X.glmnet glmnet
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 9 1.48 0.049
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.6885565 99.25138 0.5363701 117.9834 0.6600662
## min.RMSESD.fit max.RsquaredSD.fit
## 1 5.858388 0.0422371
## label step_major step_minor bgn end elapsed
## 11 fit.models_1_glmnet 11 0 217.459 220.986 3.527
## 12 fit.models_1_rpart 12 0 220.986 NA NA
## [1] "fitting model: All.Interact.X.no.rnorm.rpart"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0533 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 1859
##
## CP nsplit rel error
## 1 0.2082348 0 1.0000000
## 2 0.1528586 1 0.7917652
## 3 0.0532712 2 0.6389066
##
## Variable importance
## biddable
## 28
## prdline.my.fctriPadAir:idseq.my
## 21
## prdline.my.fctriPadAir
## 20
## idseq.my
## 6
## prdline.my.fctriPad 3+:biddable
## 5
## prdline.my.fctriPadAir:biddable
## 5
## prdline.my.fctriPad 2:biddable
## 5
## prdline.my.fctriPadmini:biddable
## 5
## prdline.my.fctriPadAir:.clusterid.fctr2
## 3
## prdline.my.fctriPadAir:.clusterid.fctr4
## 2
## prdline.my.fctriPadAir:.clusterid.fctr3
## 1
## color.fctrGold
## 1
##
## Node number 1: 1859 observations, complexity param=0.2082348
## mean=211.3404, MSE=29020.33
## left son=2 (837 obs) right son=3 (1022 obs)
## Primary splits:
## biddable < 0.5 to the right, improve=0.20823480, (0 missing)
## prdline.my.fctriPadAir:idseq.my < 34.5 to the left, improve=0.19321370, (0 missing)
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.19258840, (0 missing)
## condition.fctrNew < 0.5 to the left, improve=0.18855550, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.07643644, (0 missing)
## Surrogate splits:
## idseq.my < 869.5 to the left, agree=0.639, adj=0.197, (0 split)
## prdline.my.fctriPad 3+:biddable < 0.5 to the right, agree=0.626, adj=0.170, (0 split)
## prdline.my.fctriPadAir:biddable < 0.5 to the right, agree=0.626, adj=0.168, (0 split)
## prdline.my.fctriPad 2:biddable < 0.5 to the right, agree=0.625, adj=0.167, (0 split)
## prdline.my.fctriPadmini:biddable < 0.5 to the right, agree=0.623, adj=0.162, (0 split)
##
## Node number 2: 837 observations
## mean=125.4409, MSE=18411.58
##
## Node number 3: 1022 observations, complexity param=0.1528586
## mean=281.6905, MSE=26716.52
## left son=6 (815 obs) right son=7 (207 obs)
## Primary splits:
## prdline.my.fctriPadAir:idseq.my < 34 to the left, improve=0.3020235, (0 missing)
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.2980157, (0 missing)
## condition.fctrNew < 0.5 to the left, improve=0.1763299, (0 missing)
## prdline.my.fctriPad 1:idseq.my < 6 to the right, improve=0.1189930, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.1189930, (0 missing)
## Surrogate splits:
## prdline.my.fctriPadAir < 0.5 to the left, agree=0.995, adj=0.976, (0 split)
## prdline.my.fctriPadAir:.clusterid.fctr2 < 0.5 to the left, agree=0.824, adj=0.130, (0 split)
## prdline.my.fctriPadAir:.clusterid.fctr4 < 0.5 to the left, agree=0.814, adj=0.082, (0 split)
## prdline.my.fctriPadAir:.clusterid.fctr3 < 0.5 to the left, agree=0.812, adj=0.072, (0 split)
## color.fctrGold < 0.5 to the left, agree=0.805, adj=0.039, (0 split)
##
## Node number 6: 815 observations
## mean=236.4199, MSE=17832.66
##
## Node number 7: 207 observations
## mean=459.93, MSE=21855.7
##
## n= 1859
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 1859 53948800 211.3404
## 2) biddable>=0.5 837 15410490 125.4409 *
## 3) biddable< 0.5 1022 27304290 281.6905
## 6) prdline.my.fctriPadAir:idseq.my< 34 815 14533620 236.4199 *
## 7) prdline.my.fctriPadAir:idseq.my>=34 207 4524130 459.9300 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.Interact.X.no.rnorm.rpart rpart
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 2.168 0.128
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.3610934 133.5298 0.3212929 142.7501 0.3886849
## min.RMSESD.fit max.RsquaredSD.fit
## 1 3.122861 0.03323354
## label step_major step_minor bgn end elapsed
## 12 fit.models_1_rpart 12 0 220.986 225.602 4.616
## 13 fit.models_1_rf 13 0 225.603 NA NA
## [1] "fitting model: All.Interact.X.no.rnorm.rf"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 51 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 1859 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 1859 -none- numeric
## importance 101 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 1859 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 101 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.Interact.X.no.rnorm.rf rf
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 81.283 28.481
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.9276379 99.81446 0.5301108 118.7819 0.6555804
## min.RMSESD.fit max.RsquaredSD.fit
## 1 7.629279 0.05368373# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
# require(gdata) # needed for trim
# model_id <- "";
# indep_vars_vctr <- head(subset(glb_models_df, grepl("All\\.X\\.", model_id), select=feats)
# , 1)[, "feats"]
# indep_vars_vctr <- trim(unlist(strsplit(indep_vars_vctr, "[,]")))
# indep_vars_vctr <- setdiff(indep_vars_vctr, ".rnorm")
# easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
model_id <- "csm"; indep_vars_vctr <- c(NULL
,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
,"prdline.my.fctr*biddable"
# ,"prdline.my.fctr*startprice.log"
#,"prdline.my.fctr*idseq.my"
,"prdline.my.fctr*condition.fctr"
,"prdline.my.fctr*D.terms.n.post.stop"
#,"prdline.my.fctr*D.terms.n.post.stem"
,"prdline.my.fctr*cellular.fctr"
# ,"<feat1>:<feat2>"
)
for (method in glb_models_method_vctr) {
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
csm_mdl_id <- paste0(model_id, ".", method)
csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(model_id, ".",
method)]]); print(head(csm_featsimp_df))
}## [1] "fitting model: csm.lm"
## [1] " indep_vars: prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr"
## Aggregating results
## Fitting final model on full training set## Warning: not plotting observations with leverage one:
## 582, 892, 1450
## Warning: not plotting observations with leverage one:
## 582, 892, 1450
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -379.13 -55.01 -9.94 45.44 781.58
##
## Coefficients: (7 not defined because of singularities)
## Estimate
## (Intercept) 192.097
## `prdline.my.fctriPad 1` -88.368
## `prdline.my.fctriPad 2` -9.767
## `prdline.my.fctriPad 3+` 70.157
## prdline.my.fctriPadAir 219.826
## prdline.my.fctriPadmini 25.319
## `prdline.my.fctriPadmini 2+` 75.655
## biddable -71.512
## `condition.fctrFor parts or not working` -43.705
## `condition.fctrManufacturer refurbished` -14.741
## condition.fctrNew 84.868
## `condition.fctrNew other (see details)` 115.526
## `condition.fctrSeller refurbished` 8.620
## D.terms.n.post.stop -5.883
## cellular.fctr1 44.147
## cellular.fctrUnknown -10.660
## `prdline.my.fctrUnknown:.clusterid.fctr2` 67.726
## `prdline.my.fctriPad 1:.clusterid.fctr2` -5.586
## `prdline.my.fctriPad 2:.clusterid.fctr2` -4.052
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 16.806
## `prdline.my.fctriPadAir:.clusterid.fctr2` -33.542
## `prdline.my.fctriPadmini:.clusterid.fctr2` 13.835
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 29.463
## `prdline.my.fctrUnknown:.clusterid.fctr3` 6.243
## `prdline.my.fctriPad 1:.clusterid.fctr3` 6.142
## `prdline.my.fctriPad 2:.clusterid.fctr3` 37.002
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -5.963
## `prdline.my.fctriPadAir:.clusterid.fctr3` -6.826
## `prdline.my.fctriPadmini:.clusterid.fctr3` 7.416
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -28.715
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 13.926
## `prdline.my.fctriPad 2:.clusterid.fctr4` -31.624
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 6.718
## `prdline.my.fctriPadAir:.clusterid.fctr4` 10.025
## `prdline.my.fctriPadmini:.clusterid.fctr4` 23.297
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 19.806
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 52.835
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:biddable` 15.800
## `prdline.my.fctriPad 2:biddable` -34.995
## `prdline.my.fctriPad 3+:biddable` -65.942
## `prdline.my.fctriPadAir:biddable` -144.150
## `prdline.my.fctriPadmini:biddable` -40.960
## `prdline.my.fctriPadmini 2+:biddable` -53.093
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 7.159
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` -2.193
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -56.906
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -94.217
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` -39.773
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` -69.917
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` -54.694
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 46.556
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 26.370
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 47.056
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 24.733
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 82.206
## `prdline.my.fctriPad 1:condition.fctrNew` 94.215
## `prdline.my.fctriPad 2:condition.fctrNew` -180.922
## `prdline.my.fctriPad 3+:condition.fctrNew` 58.321
## `prdline.my.fctriPadAir:condition.fctrNew` 43.960
## `prdline.my.fctriPadmini:condition.fctrNew` -26.299
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 51.893
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` -102.468
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` -51.817
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` -17.556
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` -61.708
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` -90.598
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` -18.055
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` -36.259
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` -14.935
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` -9.427
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` -36.413
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 2.556
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` -61.370
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 5.811
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 4.696
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 5.364
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 3.607
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 2.370
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 6.962
## `prdline.my.fctriPad 1:cellular.fctr1` -21.738
## `prdline.my.fctriPad 2:cellular.fctr1` -25.373
## `prdline.my.fctriPad 3+:cellular.fctr1` -26.318
## `prdline.my.fctriPadAir:cellular.fctr1` 10.890
## `prdline.my.fctriPadmini:cellular.fctr1` 5.218
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 49.048
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 38.462
## `prdline.my.fctriPad 2:cellular.fctrUnknown` -5.626
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` -12.748
## `prdline.my.fctriPadAir:cellular.fctrUnknown` -68.227
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 10.259
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 14.354
## Std. Error
## (Intercept) 20.986
## `prdline.my.fctriPad 1` 25.086
## `prdline.my.fctriPad 2` 25.214
## `prdline.my.fctriPad 3+` 25.451
## prdline.my.fctriPadAir 24.625
## prdline.my.fctriPadmini 24.894
## `prdline.my.fctriPadmini 2+` 27.031
## biddable 16.538
## `condition.fctrFor parts or not working` 23.944
## `condition.fctrManufacturer refurbished` 112.756
## condition.fctrNew 22.295
## `condition.fctrNew other (see details)` 55.793
## `condition.fctrSeller refurbished` 36.253
## D.terms.n.post.stop 3.273
## cellular.fctr1 34.683
## cellular.fctrUnknown 19.876
## `prdline.my.fctrUnknown:.clusterid.fctr2` 29.213
## `prdline.my.fctriPad 1:.clusterid.fctr2` 31.661
## `prdline.my.fctriPad 2:.clusterid.fctr2` 22.625
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 21.061
## `prdline.my.fctriPadAir:.clusterid.fctr2` 24.167
## `prdline.my.fctriPadmini:.clusterid.fctr2` 28.588
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 44.813
## `prdline.my.fctrUnknown:.clusterid.fctr3` 33.598
## `prdline.my.fctriPad 1:.clusterid.fctr3` 32.480
## `prdline.my.fctriPad 2:.clusterid.fctr3` 31.525
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 22.870
## `prdline.my.fctriPadAir:.clusterid.fctr3` 28.414
## `prdline.my.fctriPadmini:.clusterid.fctr3` 31.087
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 39.722
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 32.689
## `prdline.my.fctriPad 2:.clusterid.fctr4` 31.252
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 23.699
## `prdline.my.fctriPadAir:.clusterid.fctr4` 28.134
## `prdline.my.fctriPadmini:.clusterid.fctr4` 34.296
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 33.928
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 38.037
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:biddable` 22.119
## `prdline.my.fctriPad 2:biddable` 21.327
## `prdline.my.fctriPad 3+:biddable` 21.480
## `prdline.my.fctriPadAir:biddable` 20.403
## `prdline.my.fctriPadmini:biddable` 21.495
## `prdline.my.fctriPadmini 2+:biddable` 23.232
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 37.100
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 31.221
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 31.811
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 36.205
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 30.681
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 60.515
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 156.585
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 122.978
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 117.260
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 118.921
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 120.427
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 126.053
## `prdline.my.fctriPad 1:condition.fctrNew` 47.820
## `prdline.my.fctriPad 2:condition.fctrNew` 115.432
## `prdline.my.fctriPad 3+:condition.fctrNew` 45.394
## `prdline.my.fctriPadAir:condition.fctrNew` 26.772
## `prdline.my.fctriPadmini:condition.fctrNew` 30.017
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 29.207
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 84.086
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 68.125
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 63.276
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 60.513
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 66.754
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 63.207
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 45.350
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 44.649
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 42.642
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 46.570
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 48.122
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` 61.864
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 4.540
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 4.032
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 3.891
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 4.050
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 4.445
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 5.497
## `prdline.my.fctriPad 1:cellular.fctr1` 38.118
## `prdline.my.fctriPad 2:cellular.fctr1` 37.770
## `prdline.my.fctriPad 3+:cellular.fctr1` 37.160
## `prdline.my.fctriPadAir:cellular.fctr1` 36.877
## `prdline.my.fctriPadmini:cellular.fctr1` 38.336
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 38.937
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 41.274
## `prdline.my.fctriPad 2:cellular.fctrUnknown` 40.552
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 33.431
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 35.081
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 36.478
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 37.477
## t value
## (Intercept) 9.154
## `prdline.my.fctriPad 1` -3.523
## `prdline.my.fctriPad 2` -0.387
## `prdline.my.fctriPad 3+` 2.757
## prdline.my.fctriPadAir 8.927
## prdline.my.fctriPadmini 1.017
## `prdline.my.fctriPadmini 2+` 2.799
## biddable -4.324
## `condition.fctrFor parts or not working` -1.825
## `condition.fctrManufacturer refurbished` -0.131
## condition.fctrNew 3.807
## `condition.fctrNew other (see details)` 2.071
## `condition.fctrSeller refurbished` 0.238
## D.terms.n.post.stop -1.797
## cellular.fctr1 1.273
## cellular.fctrUnknown -0.536
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.318
## `prdline.my.fctriPad 1:.clusterid.fctr2` -0.176
## `prdline.my.fctriPad 2:.clusterid.fctr2` -0.179
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.798
## `prdline.my.fctriPadAir:.clusterid.fctr2` -1.388
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.484
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.657
## `prdline.my.fctrUnknown:.clusterid.fctr3` 0.186
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.189
## `prdline.my.fctriPad 2:.clusterid.fctr3` 1.174
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.261
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.240
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.239
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.723
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 0.426
## `prdline.my.fctriPad 2:.clusterid.fctr4` -1.012
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.283
## `prdline.my.fctriPadAir:.clusterid.fctr4` 0.356
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.679
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.584
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 1.389
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:biddable` 0.714
## `prdline.my.fctriPad 2:biddable` -1.641
## `prdline.my.fctriPad 3+:biddable` -3.070
## `prdline.my.fctriPadAir:biddable` -7.065
## `prdline.my.fctriPadmini:biddable` -1.906
## `prdline.my.fctriPadmini 2+:biddable` -2.285
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 0.193
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` -0.070
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -1.789
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -2.602
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` -1.296
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` -1.155
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` -0.349
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 0.379
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 0.225
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 0.396
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 0.205
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 0.652
## `prdline.my.fctriPad 1:condition.fctrNew` 1.970
## `prdline.my.fctriPad 2:condition.fctrNew` -1.567
## `prdline.my.fctriPad 3+:condition.fctrNew` 1.285
## `prdline.my.fctriPadAir:condition.fctrNew` 1.642
## `prdline.my.fctriPadmini:condition.fctrNew` -0.876
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 1.777
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` -1.219
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` -0.761
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` -0.277
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` -1.020
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` -1.357
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` -0.286
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` -0.800
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` -0.334
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` -0.221
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` -0.782
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 0.053
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` -0.992
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 1.280
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 1.165
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 1.379
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 0.891
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 0.533
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 1.266
## `prdline.my.fctriPad 1:cellular.fctr1` -0.570
## `prdline.my.fctriPad 2:cellular.fctr1` -0.672
## `prdline.my.fctriPad 3+:cellular.fctr1` -0.708
## `prdline.my.fctriPadAir:cellular.fctr1` 0.295
## `prdline.my.fctriPadmini:cellular.fctr1` 0.136
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 1.260
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 0.932
## `prdline.my.fctriPad 2:cellular.fctrUnknown` -0.139
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` -0.381
## `prdline.my.fctriPadAir:cellular.fctrUnknown` -1.945
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 0.281
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 0.383
## Pr(>|t|)
## (Intercept) < 2e-16
## `prdline.my.fctriPad 1` 0.000438
## `prdline.my.fctriPad 2` 0.698551
## `prdline.my.fctriPad 3+` 0.005900
## prdline.my.fctriPadAir < 2e-16
## prdline.my.fctriPadmini 0.309248
## `prdline.my.fctriPadmini 2+` 0.005184
## biddable 1.62e-05
## `condition.fctrFor parts or not working` 0.068128
## `condition.fctrManufacturer refurbished` 0.896003
## condition.fctrNew 0.000146
## `condition.fctrNew other (see details)` 0.038539
## `condition.fctrSeller refurbished` 0.812097
## D.terms.n.post.stop 0.072454
## cellular.fctr1 0.203236
## cellular.fctrUnknown 0.591814
## `prdline.my.fctrUnknown:.clusterid.fctr2` 0.020544
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.859968
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.857884
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.424987
## `prdline.my.fctriPadAir:.clusterid.fctr2` 0.165336
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.628476
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.510965
## `prdline.my.fctrUnknown:.clusterid.fctr3` 0.852620
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.850026
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.240662
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.794335
## `prdline.my.fctriPadAir:.clusterid.fctr3` 0.810176
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.811479
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.469847
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 0.670146
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.311728
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.776837
## `prdline.my.fctriPadAir:.clusterid.fctr4` 0.721636
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.497036
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.559462
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 0.164995
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:biddable` 0.475138
## `prdline.my.fctriPad 2:biddable` 0.100996
## `prdline.my.fctriPad 3+:biddable` 0.002174
## `prdline.my.fctriPadAir:biddable` 2.30e-12
## `prdline.my.fctriPadmini:biddable` 0.056869
## `prdline.my.fctriPadmini 2+:biddable` 0.022410
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 0.847009
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 0.944017
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 0.073807
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 0.009336
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 0.195031
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 0.248096
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 0.726911
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 0.705053
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 0.822094
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 0.692378
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 0.837304
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 0.514383
## `prdline.my.fctriPad 1:condition.fctrNew` 0.048972
## `prdline.my.fctriPad 2:condition.fctrNew` 0.117213
## `prdline.my.fctriPad 3+:condition.fctrNew` 0.199041
## `prdline.my.fctriPadAir:condition.fctrNew` 0.100767
## `prdline.my.fctriPadmini:condition.fctrNew` 0.381064
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 0.075781
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 0.223157
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 0.446989
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 0.781465
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 0.307987
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 0.174893
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 0.775178
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 0.424079
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 0.738051
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 0.825059
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 0.434378
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 0.957647
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` 0.321330
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 0.200718
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 0.244213
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 0.168173
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 0.373314
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 0.593899
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 0.205522
## `prdline.my.fctriPad 1:cellular.fctr1` 0.568558
## `prdline.my.fctriPad 2:cellular.fctr1` 0.501819
## `prdline.my.fctriPad 3+:cellular.fctr1` 0.478895
## `prdline.my.fctriPadAir:cellular.fctr1` 0.767789
## `prdline.my.fctriPadmini:cellular.fctr1` 0.891756
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 0.207944
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 0.351540
## `prdline.my.fctriPad 2:cellular.fctrUnknown` 0.889674
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 0.703008
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 0.051952
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 0.778572
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 0.701764
##
## (Intercept) ***
## `prdline.my.fctriPad 1` ***
## `prdline.my.fctriPad 2`
## `prdline.my.fctriPad 3+` **
## prdline.my.fctriPadAir ***
## prdline.my.fctriPadmini
## `prdline.my.fctriPadmini 2+` **
## biddable ***
## `condition.fctrFor parts or not working` .
## `condition.fctrManufacturer refurbished`
## condition.fctrNew ***
## `condition.fctrNew other (see details)` *
## `condition.fctrSeller refurbished`
## D.terms.n.post.stop .
## cellular.fctr1
## cellular.fctrUnknown
## `prdline.my.fctrUnknown:.clusterid.fctr2` *
## `prdline.my.fctriPad 1:.clusterid.fctr2`
## `prdline.my.fctriPad 2:.clusterid.fctr2`
## `prdline.my.fctriPad 3+:.clusterid.fctr2`
## `prdline.my.fctriPadAir:.clusterid.fctr2`
## `prdline.my.fctriPadmini:.clusterid.fctr2`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2`
## `prdline.my.fctrUnknown:.clusterid.fctr3`
## `prdline.my.fctriPad 1:.clusterid.fctr3`
## `prdline.my.fctriPad 2:.clusterid.fctr3`
## `prdline.my.fctriPad 3+:.clusterid.fctr3`
## `prdline.my.fctriPadAir:.clusterid.fctr3`
## `prdline.my.fctriPadmini:.clusterid.fctr3`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3`
## `prdline.my.fctrUnknown:.clusterid.fctr4`
## `prdline.my.fctriPad 1:.clusterid.fctr4`
## `prdline.my.fctriPad 2:.clusterid.fctr4`
## `prdline.my.fctriPad 3+:.clusterid.fctr4`
## `prdline.my.fctriPadAir:.clusterid.fctr4`
## `prdline.my.fctriPadmini:.clusterid.fctr4`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4`
## `prdline.my.fctrUnknown:.clusterid.fctr5`
## `prdline.my.fctriPad 1:.clusterid.fctr5`
## `prdline.my.fctriPad 2:.clusterid.fctr5`
## `prdline.my.fctriPad 3+:.clusterid.fctr5`
## `prdline.my.fctriPadAir:.clusterid.fctr5`
## `prdline.my.fctriPadmini:.clusterid.fctr5`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5`
## `prdline.my.fctriPad 1:biddable`
## `prdline.my.fctriPad 2:biddable`
## `prdline.my.fctriPad 3+:biddable` **
## `prdline.my.fctriPadAir:biddable` ***
## `prdline.my.fctriPadmini:biddable` .
## `prdline.my.fctriPadmini 2+:biddable` *
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` .
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` **
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working`
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 1:condition.fctrNew` *
## `prdline.my.fctriPad 2:condition.fctrNew`
## `prdline.my.fctriPad 3+:condition.fctrNew`
## `prdline.my.fctriPadAir:condition.fctrNew`
## `prdline.my.fctriPadmini:condition.fctrNew`
## `prdline.my.fctriPadmini 2+:condition.fctrNew` .
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)`
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)`
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)`
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished`
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished`
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 1:D.terms.n.post.stop`
## `prdline.my.fctriPad 2:D.terms.n.post.stop`
## `prdline.my.fctriPad 3+:D.terms.n.post.stop`
## `prdline.my.fctriPadAir:D.terms.n.post.stop`
## `prdline.my.fctriPadmini:D.terms.n.post.stop`
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop`
## `prdline.my.fctriPad 1:cellular.fctr1`
## `prdline.my.fctriPad 2:cellular.fctr1`
## `prdline.my.fctriPad 3+:cellular.fctr1`
## `prdline.my.fctriPadAir:cellular.fctr1`
## `prdline.my.fctriPadmini:cellular.fctr1`
## `prdline.my.fctriPadmini 2+:cellular.fctr1`
## `prdline.my.fctriPad 1:cellular.fctrUnknown`
## `prdline.my.fctriPad 2:cellular.fctrUnknown`
## `prdline.my.fctriPad 3+:cellular.fctrUnknown`
## `prdline.my.fctriPadAir:cellular.fctrUnknown` .
## `prdline.my.fctriPadmini:cellular.fctrUnknown`
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 107.5 on 1768 degrees of freedom
## Multiple R-squared: 0.6212, Adjusted R-squared: 0.6019
## F-statistic: 32.22 on 90 and 1768 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading## model_id model_method
## 1 csm.lm lm
## feats
## 1 prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.269 0.073
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.6212169 111.2592 0.5420392 117.2598 0.601935
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.5752187 5.391855 0.04061329
## importance
## prdline.my.fctriPadAir 100.00000
## `prdline.my.fctriPadAir:biddable` 79.02049
## biddable 48.13018
## condition.fctrNew 42.29953
## `prdline.my.fctriPad 1` 39.09915
## `prdline.my.fctriPad 3+:biddable` 33.99736
## [1] "fitting model: csm.glm"
## [1] " indep_vars: prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr"
## Aggregating results
## Fitting final model on full training set## Warning: not plotting observations with leverage one:
## 582, 892, 1450
## Warning: not plotting observations with leverage one:
## 582, 892, 1450
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -379.13 -55.01 -9.94 45.44 781.58
##
## Coefficients: (7 not defined because of singularities)
## Estimate
## (Intercept) 192.097
## `prdline.my.fctriPad 1` -88.368
## `prdline.my.fctriPad 2` -9.767
## `prdline.my.fctriPad 3+` 70.157
## prdline.my.fctriPadAir 219.826
## prdline.my.fctriPadmini 25.319
## `prdline.my.fctriPadmini 2+` 75.655
## biddable -71.512
## `condition.fctrFor parts or not working` -43.705
## `condition.fctrManufacturer refurbished` -14.741
## condition.fctrNew 84.868
## `condition.fctrNew other (see details)` 115.526
## `condition.fctrSeller refurbished` 8.620
## D.terms.n.post.stop -5.883
## cellular.fctr1 44.147
## cellular.fctrUnknown -10.660
## `prdline.my.fctrUnknown:.clusterid.fctr2` 67.726
## `prdline.my.fctriPad 1:.clusterid.fctr2` -5.586
## `prdline.my.fctriPad 2:.clusterid.fctr2` -4.052
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 16.806
## `prdline.my.fctriPadAir:.clusterid.fctr2` -33.542
## `prdline.my.fctriPadmini:.clusterid.fctr2` 13.835
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 29.463
## `prdline.my.fctrUnknown:.clusterid.fctr3` 6.243
## `prdline.my.fctriPad 1:.clusterid.fctr3` 6.142
## `prdline.my.fctriPad 2:.clusterid.fctr3` 37.002
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -5.963
## `prdline.my.fctriPadAir:.clusterid.fctr3` -6.826
## `prdline.my.fctriPadmini:.clusterid.fctr3` 7.416
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -28.715
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 13.926
## `prdline.my.fctriPad 2:.clusterid.fctr4` -31.624
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 6.718
## `prdline.my.fctriPadAir:.clusterid.fctr4` 10.025
## `prdline.my.fctriPadmini:.clusterid.fctr4` 23.297
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 19.806
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 52.835
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:biddable` 15.800
## `prdline.my.fctriPad 2:biddable` -34.995
## `prdline.my.fctriPad 3+:biddable` -65.942
## `prdline.my.fctriPadAir:biddable` -144.150
## `prdline.my.fctriPadmini:biddable` -40.960
## `prdline.my.fctriPadmini 2+:biddable` -53.093
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 7.159
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` -2.193
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -56.906
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -94.217
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` -39.773
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` -69.917
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` -54.694
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 46.556
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 26.370
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 47.056
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 24.733
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 82.206
## `prdline.my.fctriPad 1:condition.fctrNew` 94.215
## `prdline.my.fctriPad 2:condition.fctrNew` -180.922
## `prdline.my.fctriPad 3+:condition.fctrNew` 58.321
## `prdline.my.fctriPadAir:condition.fctrNew` 43.960
## `prdline.my.fctriPadmini:condition.fctrNew` -26.299
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 51.893
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` -102.468
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` -51.817
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` -17.556
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` -61.708
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` -90.598
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` -18.055
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` -36.259
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` -14.935
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` -9.427
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` -36.413
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 2.556
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` -61.370
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 5.811
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 4.696
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 5.364
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 3.607
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 2.370
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 6.962
## `prdline.my.fctriPad 1:cellular.fctr1` -21.738
## `prdline.my.fctriPad 2:cellular.fctr1` -25.373
## `prdline.my.fctriPad 3+:cellular.fctr1` -26.318
## `prdline.my.fctriPadAir:cellular.fctr1` 10.890
## `prdline.my.fctriPadmini:cellular.fctr1` 5.218
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 49.048
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 38.462
## `prdline.my.fctriPad 2:cellular.fctrUnknown` -5.626
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` -12.748
## `prdline.my.fctriPadAir:cellular.fctrUnknown` -68.227
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 10.259
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 14.354
## Std. Error
## (Intercept) 20.986
## `prdline.my.fctriPad 1` 25.086
## `prdline.my.fctriPad 2` 25.214
## `prdline.my.fctriPad 3+` 25.451
## prdline.my.fctriPadAir 24.625
## prdline.my.fctriPadmini 24.894
## `prdline.my.fctriPadmini 2+` 27.031
## biddable 16.538
## `condition.fctrFor parts or not working` 23.944
## `condition.fctrManufacturer refurbished` 112.756
## condition.fctrNew 22.295
## `condition.fctrNew other (see details)` 55.793
## `condition.fctrSeller refurbished` 36.253
## D.terms.n.post.stop 3.273
## cellular.fctr1 34.683
## cellular.fctrUnknown 19.876
## `prdline.my.fctrUnknown:.clusterid.fctr2` 29.213
## `prdline.my.fctriPad 1:.clusterid.fctr2` 31.661
## `prdline.my.fctriPad 2:.clusterid.fctr2` 22.625
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 21.061
## `prdline.my.fctriPadAir:.clusterid.fctr2` 24.167
## `prdline.my.fctriPadmini:.clusterid.fctr2` 28.588
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 44.813
## `prdline.my.fctrUnknown:.clusterid.fctr3` 33.598
## `prdline.my.fctriPad 1:.clusterid.fctr3` 32.480
## `prdline.my.fctriPad 2:.clusterid.fctr3` 31.525
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 22.870
## `prdline.my.fctriPadAir:.clusterid.fctr3` 28.414
## `prdline.my.fctriPadmini:.clusterid.fctr3` 31.087
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 39.722
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 32.689
## `prdline.my.fctriPad 2:.clusterid.fctr4` 31.252
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 23.699
## `prdline.my.fctriPadAir:.clusterid.fctr4` 28.134
## `prdline.my.fctriPadmini:.clusterid.fctr4` 34.296
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 33.928
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 38.037
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:biddable` 22.119
## `prdline.my.fctriPad 2:biddable` 21.327
## `prdline.my.fctriPad 3+:biddable` 21.480
## `prdline.my.fctriPadAir:biddable` 20.403
## `prdline.my.fctriPadmini:biddable` 21.495
## `prdline.my.fctriPadmini 2+:biddable` 23.232
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 37.100
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 31.221
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 31.811
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 36.205
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 30.681
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 60.515
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 156.585
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 122.978
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 117.260
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 118.921
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 120.427
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 126.053
## `prdline.my.fctriPad 1:condition.fctrNew` 47.820
## `prdline.my.fctriPad 2:condition.fctrNew` 115.432
## `prdline.my.fctriPad 3+:condition.fctrNew` 45.394
## `prdline.my.fctriPadAir:condition.fctrNew` 26.772
## `prdline.my.fctriPadmini:condition.fctrNew` 30.017
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 29.207
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 84.086
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 68.125
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 63.276
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 60.513
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 66.754
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 63.207
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 45.350
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 44.649
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 42.642
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 46.570
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 48.122
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` 61.864
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 4.540
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 4.032
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 3.891
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 4.050
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 4.445
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 5.497
## `prdline.my.fctriPad 1:cellular.fctr1` 38.118
## `prdline.my.fctriPad 2:cellular.fctr1` 37.770
## `prdline.my.fctriPad 3+:cellular.fctr1` 37.160
## `prdline.my.fctriPadAir:cellular.fctr1` 36.877
## `prdline.my.fctriPadmini:cellular.fctr1` 38.336
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 38.937
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 41.274
## `prdline.my.fctriPad 2:cellular.fctrUnknown` 40.552
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 33.431
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 35.081
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 36.478
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 37.477
## t value
## (Intercept) 9.154
## `prdline.my.fctriPad 1` -3.523
## `prdline.my.fctriPad 2` -0.387
## `prdline.my.fctriPad 3+` 2.757
## prdline.my.fctriPadAir 8.927
## prdline.my.fctriPadmini 1.017
## `prdline.my.fctriPadmini 2+` 2.799
## biddable -4.324
## `condition.fctrFor parts or not working` -1.825
## `condition.fctrManufacturer refurbished` -0.131
## condition.fctrNew 3.807
## `condition.fctrNew other (see details)` 2.071
## `condition.fctrSeller refurbished` 0.238
## D.terms.n.post.stop -1.797
## cellular.fctr1 1.273
## cellular.fctrUnknown -0.536
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.318
## `prdline.my.fctriPad 1:.clusterid.fctr2` -0.176
## `prdline.my.fctriPad 2:.clusterid.fctr2` -0.179
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.798
## `prdline.my.fctriPadAir:.clusterid.fctr2` -1.388
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.484
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.657
## `prdline.my.fctrUnknown:.clusterid.fctr3` 0.186
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.189
## `prdline.my.fctriPad 2:.clusterid.fctr3` 1.174
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.261
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.240
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.239
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.723
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 0.426
## `prdline.my.fctriPad 2:.clusterid.fctr4` -1.012
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.283
## `prdline.my.fctriPadAir:.clusterid.fctr4` 0.356
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.679
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.584
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 1.389
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:biddable` 0.714
## `prdline.my.fctriPad 2:biddable` -1.641
## `prdline.my.fctriPad 3+:biddable` -3.070
## `prdline.my.fctriPadAir:biddable` -7.065
## `prdline.my.fctriPadmini:biddable` -1.906
## `prdline.my.fctriPadmini 2+:biddable` -2.285
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 0.193
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` -0.070
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -1.789
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -2.602
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` -1.296
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` -1.155
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` -0.349
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 0.379
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 0.225
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 0.396
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 0.205
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 0.652
## `prdline.my.fctriPad 1:condition.fctrNew` 1.970
## `prdline.my.fctriPad 2:condition.fctrNew` -1.567
## `prdline.my.fctriPad 3+:condition.fctrNew` 1.285
## `prdline.my.fctriPadAir:condition.fctrNew` 1.642
## `prdline.my.fctriPadmini:condition.fctrNew` -0.876
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 1.777
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` -1.219
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` -0.761
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` -0.277
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` -1.020
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` -1.357
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` -0.286
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` -0.800
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` -0.334
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` -0.221
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` -0.782
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 0.053
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` -0.992
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 1.280
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 1.165
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 1.379
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 0.891
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 0.533
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 1.266
## `prdline.my.fctriPad 1:cellular.fctr1` -0.570
## `prdline.my.fctriPad 2:cellular.fctr1` -0.672
## `prdline.my.fctriPad 3+:cellular.fctr1` -0.708
## `prdline.my.fctriPadAir:cellular.fctr1` 0.295
## `prdline.my.fctriPadmini:cellular.fctr1` 0.136
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 1.260
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 0.932
## `prdline.my.fctriPad 2:cellular.fctrUnknown` -0.139
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` -0.381
## `prdline.my.fctriPadAir:cellular.fctrUnknown` -1.945
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 0.281
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 0.383
## Pr(>|t|)
## (Intercept) < 2e-16
## `prdline.my.fctriPad 1` 0.000438
## `prdline.my.fctriPad 2` 0.698551
## `prdline.my.fctriPad 3+` 0.005900
## prdline.my.fctriPadAir < 2e-16
## prdline.my.fctriPadmini 0.309248
## `prdline.my.fctriPadmini 2+` 0.005184
## biddable 1.62e-05
## `condition.fctrFor parts or not working` 0.068128
## `condition.fctrManufacturer refurbished` 0.896003
## condition.fctrNew 0.000146
## `condition.fctrNew other (see details)` 0.038539
## `condition.fctrSeller refurbished` 0.812097
## D.terms.n.post.stop 0.072454
## cellular.fctr1 0.203236
## cellular.fctrUnknown 0.591814
## `prdline.my.fctrUnknown:.clusterid.fctr2` 0.020544
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.859968
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.857884
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.424987
## `prdline.my.fctriPadAir:.clusterid.fctr2` 0.165336
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.628476
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.510965
## `prdline.my.fctrUnknown:.clusterid.fctr3` 0.852620
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.850026
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.240662
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.794335
## `prdline.my.fctriPadAir:.clusterid.fctr3` 0.810176
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.811479
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.469847
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 0.670146
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.311728
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.776837
## `prdline.my.fctriPadAir:.clusterid.fctr4` 0.721636
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.497036
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.559462
## `prdline.my.fctriPad 3+:.clusterid.fctr5` NA
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 0.164995
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:biddable` 0.475138
## `prdline.my.fctriPad 2:biddable` 0.100996
## `prdline.my.fctriPad 3+:biddable` 0.002174
## `prdline.my.fctriPadAir:biddable` 2.30e-12
## `prdline.my.fctriPadmini:biddable` 0.056869
## `prdline.my.fctriPadmini 2+:biddable` 0.022410
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 0.847009
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 0.944017
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 0.073807
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 0.009336
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 0.195031
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 0.248096
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 0.726911
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 0.705053
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 0.822094
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 0.692378
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 0.837304
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 0.514383
## `prdline.my.fctriPad 1:condition.fctrNew` 0.048972
## `prdline.my.fctriPad 2:condition.fctrNew` 0.117213
## `prdline.my.fctriPad 3+:condition.fctrNew` 0.199041
## `prdline.my.fctriPadAir:condition.fctrNew` 0.100767
## `prdline.my.fctriPadmini:condition.fctrNew` 0.381064
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 0.075781
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 0.223157
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 0.446989
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 0.781465
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 0.307987
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 0.174893
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 0.775178
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 0.424079
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 0.738051
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 0.825059
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 0.434378
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 0.957647
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` 0.321330
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 0.200718
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 0.244213
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 0.168173
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 0.373314
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 0.593899
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 0.205522
## `prdline.my.fctriPad 1:cellular.fctr1` 0.568558
## `prdline.my.fctriPad 2:cellular.fctr1` 0.501819
## `prdline.my.fctriPad 3+:cellular.fctr1` 0.478895
## `prdline.my.fctriPadAir:cellular.fctr1` 0.767789
## `prdline.my.fctriPadmini:cellular.fctr1` 0.891756
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 0.207944
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 0.351540
## `prdline.my.fctriPad 2:cellular.fctrUnknown` 0.889674
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 0.703008
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 0.051952
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 0.778572
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 0.701764
##
## (Intercept) ***
## `prdline.my.fctriPad 1` ***
## `prdline.my.fctriPad 2`
## `prdline.my.fctriPad 3+` **
## prdline.my.fctriPadAir ***
## prdline.my.fctriPadmini
## `prdline.my.fctriPadmini 2+` **
## biddable ***
## `condition.fctrFor parts or not working` .
## `condition.fctrManufacturer refurbished`
## condition.fctrNew ***
## `condition.fctrNew other (see details)` *
## `condition.fctrSeller refurbished`
## D.terms.n.post.stop .
## cellular.fctr1
## cellular.fctrUnknown
## `prdline.my.fctrUnknown:.clusterid.fctr2` *
## `prdline.my.fctriPad 1:.clusterid.fctr2`
## `prdline.my.fctriPad 2:.clusterid.fctr2`
## `prdline.my.fctriPad 3+:.clusterid.fctr2`
## `prdline.my.fctriPadAir:.clusterid.fctr2`
## `prdline.my.fctriPadmini:.clusterid.fctr2`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2`
## `prdline.my.fctrUnknown:.clusterid.fctr3`
## `prdline.my.fctriPad 1:.clusterid.fctr3`
## `prdline.my.fctriPad 2:.clusterid.fctr3`
## `prdline.my.fctriPad 3+:.clusterid.fctr3`
## `prdline.my.fctriPadAir:.clusterid.fctr3`
## `prdline.my.fctriPadmini:.clusterid.fctr3`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3`
## `prdline.my.fctrUnknown:.clusterid.fctr4`
## `prdline.my.fctriPad 1:.clusterid.fctr4`
## `prdline.my.fctriPad 2:.clusterid.fctr4`
## `prdline.my.fctriPad 3+:.clusterid.fctr4`
## `prdline.my.fctriPadAir:.clusterid.fctr4`
## `prdline.my.fctriPadmini:.clusterid.fctr4`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4`
## `prdline.my.fctrUnknown:.clusterid.fctr5`
## `prdline.my.fctriPad 1:.clusterid.fctr5`
## `prdline.my.fctriPad 2:.clusterid.fctr5`
## `prdline.my.fctriPad 3+:.clusterid.fctr5`
## `prdline.my.fctriPadAir:.clusterid.fctr5`
## `prdline.my.fctriPadmini:.clusterid.fctr5`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5`
## `prdline.my.fctriPad 1:biddable`
## `prdline.my.fctriPad 2:biddable`
## `prdline.my.fctriPad 3+:biddable` **
## `prdline.my.fctriPadAir:biddable` ***
## `prdline.my.fctriPadmini:biddable` .
## `prdline.my.fctriPadmini 2+:biddable` *
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` .
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` **
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working`
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 1:condition.fctrNew` *
## `prdline.my.fctriPad 2:condition.fctrNew`
## `prdline.my.fctriPad 3+:condition.fctrNew`
## `prdline.my.fctriPadAir:condition.fctrNew`
## `prdline.my.fctriPadmini:condition.fctrNew`
## `prdline.my.fctriPadmini 2+:condition.fctrNew` .
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)`
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)`
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)`
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished`
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished`
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 1:D.terms.n.post.stop`
## `prdline.my.fctriPad 2:D.terms.n.post.stop`
## `prdline.my.fctriPad 3+:D.terms.n.post.stop`
## `prdline.my.fctriPadAir:D.terms.n.post.stop`
## `prdline.my.fctriPadmini:D.terms.n.post.stop`
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop`
## `prdline.my.fctriPad 1:cellular.fctr1`
## `prdline.my.fctriPad 2:cellular.fctr1`
## `prdline.my.fctriPad 3+:cellular.fctr1`
## `prdline.my.fctriPadAir:cellular.fctr1`
## `prdline.my.fctriPadmini:cellular.fctr1`
## `prdline.my.fctriPadmini 2+:cellular.fctr1`
## `prdline.my.fctriPad 1:cellular.fctrUnknown`
## `prdline.my.fctriPad 2:cellular.fctrUnknown`
## `prdline.my.fctriPad 3+:cellular.fctrUnknown`
## `prdline.my.fctriPadAir:cellular.fctrUnknown` .
## `prdline.my.fctriPadmini:cellular.fctrUnknown`
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 11558.2)
##
## Null deviance: 53948796 on 1858 degrees of freedom
## Residual deviance: 20434892 on 1768 degrees of freedom
## AIC: 22758
##
## Number of Fisher Scoring iterations: 2
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## model_id model_method
## 1 csm.glm glm
## feats
## 1 prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.349 0.12
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB min.aic.fit
## 1 0.6212169 111.2592 0.5420392 117.2598 22757.53
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.5752187 5.391855 0.04061329
## importance
## prdline.my.fctriPadAir 100.00000
## `prdline.my.fctriPadAir:biddable` 79.02049
## biddable 48.13018
## condition.fctrNew 42.29953
## `prdline.my.fctriPad 1` 39.09915
## `prdline.my.fctriPad 3+:biddable` 33.99736
## [1] "fitting model: csm.bayesglm"
## [1] " indep_vars: prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -379.21 -54.90 -9.96 45.43 781.62
##
## Coefficients:
## Estimate
## (Intercept) 192.798
## `prdline.my.fctriPad 1` -89.005
## `prdline.my.fctriPad 2` -10.533
## `prdline.my.fctriPad 3+` 69.402
## prdline.my.fctriPadAir 218.981
## prdline.my.fctriPadmini 24.579
## `prdline.my.fctriPadmini 2+` 74.893
## biddable -71.935
## `condition.fctrFor parts or not working` -44.128
## `condition.fctrManufacturer refurbished` -9.542
## condition.fctrNew 84.446
## `condition.fctrNew other (see details)` 111.881
## `condition.fctrSeller refurbished` 7.733
## D.terms.n.post.stop -5.878
## cellular.fctr1 43.412
## cellular.fctrUnknown -10.902
## `prdline.my.fctrUnknown:.clusterid.fctr2` 67.678
## `prdline.my.fctriPad 1:.clusterid.fctr2` -5.644
## `prdline.my.fctriPad 2:.clusterid.fctr2` -4.062
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 16.785
## `prdline.my.fctriPadAir:.clusterid.fctr2` -33.478
## `prdline.my.fctriPadmini:.clusterid.fctr2` 13.695
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 29.214
## `prdline.my.fctrUnknown:.clusterid.fctr3` 6.097
## `prdline.my.fctriPad 1:.clusterid.fctr3` 6.111
## `prdline.my.fctriPad 2:.clusterid.fctr3` 36.914
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -5.953
## `prdline.my.fctriPadAir:.clusterid.fctr3` -6.815
## `prdline.my.fctriPadmini:.clusterid.fctr3` 7.260
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -28.772
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.000
## `prdline.my.fctriPad 1:.clusterid.fctr4` 13.871
## `prdline.my.fctriPad 2:.clusterid.fctr4` -31.604
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 6.714
## `prdline.my.fctriPadAir:.clusterid.fctr4` 10.010
## `prdline.my.fctriPadmini:.clusterid.fctr4` 23.049
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 19.797
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.000
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.000
## `prdline.my.fctriPadmini:.clusterid.fctr5` 52.564
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.000
## `prdline.my.fctriPad 1:biddable` 16.164
## `prdline.my.fctriPad 2:biddable` -34.518
## `prdline.my.fctriPad 3+:biddable` -65.477
## `prdline.my.fctriPadAir:biddable` -143.640
## `prdline.my.fctriPadmini:biddable` -40.509
## `prdline.my.fctriPadmini 2+:biddable` -52.602
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 7.573
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` -1.713
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -56.421
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -93.599
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` -39.302
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` -68.908
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` -58.072
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 41.090
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 21.136
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 41.818
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 19.481
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 76.414
## `prdline.my.fctriPad 1:condition.fctrNew` 94.171
## `prdline.my.fctriPad 2:condition.fctrNew` -174.692
## `prdline.my.fctriPad 3+:condition.fctrNew` 58.535
## `prdline.my.fctriPadAir:condition.fctrNew` 44.466
## `prdline.my.fctriPadmini:condition.fctrNew` -25.827
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 52.355
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` -97.824
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` -47.954
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` -13.860
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` -57.915
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` -86.587
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` -14.290
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` -35.314
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` -14.013
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` -8.513
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` -35.380
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 3.450
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` -60.024
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 5.807
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 4.698
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 5.362
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 3.603
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 2.386
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 6.976
## `prdline.my.fctriPad 1:cellular.fctr1` -21.043
## `prdline.my.fctriPad 2:cellular.fctr1` -24.611
## `prdline.my.fctriPad 3+:cellular.fctr1` -25.551
## `prdline.my.fctriPadAir:cellular.fctr1` 11.670
## `prdline.my.fctriPadmini:cellular.fctr1` 5.958
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 49.733
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 38.566
## `prdline.my.fctriPad 2:cellular.fctrUnknown` -5.880
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` -12.449
## `prdline.my.fctriPadAir:cellular.fctrUnknown` -67.810
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 10.486
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 14.553
## Std. Error
## (Intercept) 20.845
## `prdline.my.fctriPad 1` 24.958
## `prdline.my.fctriPad 2` 25.086
## `prdline.my.fctriPad 3+` 25.324
## prdline.my.fctriPadAir 24.497
## prdline.my.fctriPadmini 24.764
## `prdline.my.fctriPadmini 2+` 26.901
## biddable 16.493
## `condition.fctrFor parts or not working` 23.841
## `condition.fctrManufacturer refurbished` 101.470
## condition.fctrNew 22.200
## `condition.fctrNew other (see details)` 54.310
## `condition.fctrSeller refurbished` 35.864
## D.terms.n.post.stop 3.273
## cellular.fctr1 34.109
## cellular.fctrUnknown 19.764
## `prdline.my.fctrUnknown:.clusterid.fctr2` 29.189
## `prdline.my.fctriPad 1:.clusterid.fctr2` 31.646
## `prdline.my.fctriPad 2:.clusterid.fctr2` 22.632
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 21.083
## `prdline.my.fctriPadAir:.clusterid.fctr2` 24.180
## `prdline.my.fctriPadmini:.clusterid.fctr2` 28.580
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 44.729
## `prdline.my.fctrUnknown:.clusterid.fctr3` 33.583
## `prdline.my.fctriPad 1:.clusterid.fctr3` 32.465
## `prdline.my.fctriPad 2:.clusterid.fctr3` 31.525
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 22.893
## `prdline.my.fctriPadAir:.clusterid.fctr3` 28.427
## `prdline.my.fctriPadmini:.clusterid.fctr3` 31.070
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 39.664
## `prdline.my.fctrUnknown:.clusterid.fctr4` 853.827
## `prdline.my.fctriPad 1:.clusterid.fctr4` 32.677
## `prdline.my.fctriPad 2:.clusterid.fctr4` 31.259
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 23.723
## `prdline.my.fctriPadAir:.clusterid.fctr4` 28.148
## `prdline.my.fctriPadmini:.clusterid.fctr4` 34.270
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 853.827
## `prdline.my.fctrUnknown:.clusterid.fctr5` 853.827
## `prdline.my.fctriPad 1:.clusterid.fctr5` 853.827
## `prdline.my.fctriPad 2:.clusterid.fctr5` 33.930
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 853.827
## `prdline.my.fctriPadAir:.clusterid.fctr5` 853.827
## `prdline.my.fctriPadmini:.clusterid.fctr5` 38.015
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 853.827
## `prdline.my.fctriPad 1:biddable` 22.088
## `prdline.my.fctriPad 2:biddable` 21.294
## `prdline.my.fctriPad 3+:biddable` 21.449
## `prdline.my.fctriPadAir:biddable` 20.372
## `prdline.my.fctriPadmini:biddable` 21.464
## `prdline.my.fctriPadmini 2+:biddable` 23.204
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 37.003
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 31.134
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 31.729
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 36.121
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 30.595
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 60.277
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 145.489
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 112.100
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 106.186
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 107.912
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 109.471
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 115.252
## `prdline.my.fctriPad 1:condition.fctrNew` 47.705
## `prdline.my.fctriPad 2:condition.fctrNew` 113.668
## `prdline.my.fctriPad 3+:condition.fctrNew` 45.292
## `prdline.my.fctriPadAir:condition.fctrNew` 26.689
## `prdline.my.fctriPadmini:condition.fctrNew` 29.940
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 29.118
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 82.577
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 66.730
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 61.866
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 59.084
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 65.356
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 61.792
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 44.995
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 44.294
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 42.283
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 46.216
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 47.765
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` 61.431
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 4.538
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 4.032
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 3.892
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 4.051
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 4.443
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 5.493
## `prdline.my.fctriPad 1:cellular.fctr1` 37.572
## `prdline.my.fctriPad 2:cellular.fctr1` 37.227
## `prdline.my.fctriPad 3+:cellular.fctr1` 36.613
## `prdline.my.fctriPadAir:cellular.fctr1` 36.328
## `prdline.my.fctriPadmini:cellular.fctr1` 37.800
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 38.413
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 41.188
## `prdline.my.fctriPad 2:cellular.fctrUnknown` 40.424
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 33.359
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 35.008
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 36.403
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 37.402
## t value
## (Intercept) 9.249
## `prdline.my.fctriPad 1` -3.566
## `prdline.my.fctriPad 2` -0.420
## `prdline.my.fctriPad 3+` 2.741
## prdline.my.fctriPadAir 8.939
## prdline.my.fctriPadmini 0.993
## `prdline.my.fctriPadmini 2+` 2.784
## biddable -4.362
## `condition.fctrFor parts or not working` -1.851
## `condition.fctrManufacturer refurbished` -0.094
## condition.fctrNew 3.804
## `condition.fctrNew other (see details)` 2.060
## `condition.fctrSeller refurbished` 0.216
## D.terms.n.post.stop -1.796
## cellular.fctr1 1.273
## cellular.fctrUnknown -0.552
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.319
## `prdline.my.fctriPad 1:.clusterid.fctr2` -0.178
## `prdline.my.fctriPad 2:.clusterid.fctr2` -0.179
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.796
## `prdline.my.fctriPadAir:.clusterid.fctr2` -1.385
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.479
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.653
## `prdline.my.fctrUnknown:.clusterid.fctr3` 0.182
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.188
## `prdline.my.fctriPad 2:.clusterid.fctr3` 1.171
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.260
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.240
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.234
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.725
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.000
## `prdline.my.fctriPad 1:.clusterid.fctr4` 0.424
## `prdline.my.fctriPad 2:.clusterid.fctr4` -1.011
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.283
## `prdline.my.fctriPadAir:.clusterid.fctr4` 0.356
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.673
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.583
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.000
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.000
## `prdline.my.fctriPadmini:.clusterid.fctr5` 1.383
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.000
## `prdline.my.fctriPad 1:biddable` 0.732
## `prdline.my.fctriPad 2:biddable` -1.621
## `prdline.my.fctriPad 3+:biddable` -3.053
## `prdline.my.fctriPadAir:biddable` -7.051
## `prdline.my.fctriPadmini:biddable` -1.887
## `prdline.my.fctriPadmini 2+:biddable` -2.267
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 0.205
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` -0.055
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -1.778
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -2.591
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` -1.285
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` -1.143
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` -0.399
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 0.367
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 0.199
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 0.388
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 0.178
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 0.663
## `prdline.my.fctriPad 1:condition.fctrNew` 1.974
## `prdline.my.fctriPad 2:condition.fctrNew` -1.537
## `prdline.my.fctriPad 3+:condition.fctrNew` 1.292
## `prdline.my.fctriPadAir:condition.fctrNew` 1.666
## `prdline.my.fctriPadmini:condition.fctrNew` -0.863
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 1.798
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` -1.185
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` -0.719
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` -0.224
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` -0.980
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` -1.325
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` -0.231
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` -0.785
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` -0.316
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` -0.201
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` -0.766
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 0.072
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` -0.977
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 1.280
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 1.165
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 1.378
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 0.890
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 0.537
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 1.270
## `prdline.my.fctriPad 1:cellular.fctr1` -0.560
## `prdline.my.fctriPad 2:cellular.fctr1` -0.661
## `prdline.my.fctriPad 3+:cellular.fctr1` -0.698
## `prdline.my.fctriPadAir:cellular.fctr1` 0.321
## `prdline.my.fctriPadmini:cellular.fctr1` 0.158
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 1.295
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 0.936
## `prdline.my.fctriPad 2:cellular.fctrUnknown` -0.145
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` -0.373
## `prdline.my.fctriPadAir:cellular.fctrUnknown` -1.937
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 0.288
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 0.389
## Pr(>|t|)
## (Intercept) < 2e-16
## `prdline.my.fctriPad 1` 0.000372
## `prdline.my.fctriPad 2` 0.674610
## `prdline.my.fctriPad 3+` 0.006195
## prdline.my.fctriPadAir < 2e-16
## prdline.my.fctriPadmini 0.321073
## `prdline.my.fctriPadmini 2+` 0.005426
## biddable 1.37e-05
## `condition.fctrFor parts or not working` 0.064347
## `condition.fctrManufacturer refurbished` 0.925088
## condition.fctrNew 0.000147
## `condition.fctrNew other (see details)` 0.039542
## `condition.fctrSeller refurbished` 0.829317
## D.terms.n.post.stop 0.072629
## cellular.fctr1 0.203274
## cellular.fctrUnknown 0.581281
## `prdline.my.fctrUnknown:.clusterid.fctr2` 0.020530
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.858479
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.857566
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.426057
## `prdline.my.fctriPadAir:.clusterid.fctr2` 0.166376
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.631858
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.513755
## `prdline.my.fctrUnknown:.clusterid.fctr3` 0.855959
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.850719
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.241782
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.794859
## `prdline.my.fctriPadAir:.clusterid.fctr3` 0.810556
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.815269
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.468303
## `prdline.my.fctrUnknown:.clusterid.fctr4` 1.000000
## `prdline.my.fctriPad 1:.clusterid.fctr4` 0.671256
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.312124
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.777182
## `prdline.my.fctriPadAir:.clusterid.fctr4` 0.722159
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.501315
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 1.000000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 1.000000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 1.000000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.559659
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 1.000000
## `prdline.my.fctriPadAir:.clusterid.fctr5` 1.000000
## `prdline.my.fctriPadmini:.clusterid.fctr5` 0.166930
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 1.000000
## `prdline.my.fctriPad 1:biddable` 0.464372
## `prdline.my.fctriPad 2:biddable` 0.105189
## `prdline.my.fctriPad 3+:biddable` 0.002302
## `prdline.my.fctriPadAir:biddable` 2.55e-12
## `prdline.my.fctriPadmini:biddable` 0.059289
## `prdline.my.fctriPadmini 2+:biddable` 0.023512
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 0.837861
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 0.956139
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 0.075538
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 0.009642
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 0.199098
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 0.253113
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 0.689833
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 0.714003
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 0.842252
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 0.698418
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 0.858777
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 0.507406
## `prdline.my.fctriPad 1:condition.fctrNew` 0.048537
## `prdline.my.fctriPad 2:condition.fctrNew` 0.124509
## `prdline.my.fctriPad 3+:condition.fctrNew` 0.196386
## `prdline.my.fctriPadAir:condition.fctrNew` 0.095882
## `prdline.my.fctriPadmini:condition.fctrNew` 0.388457
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 0.072346
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 0.236320
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 0.472470
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 0.822762
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 0.327109
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 0.185388
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 0.817142
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 0.432654
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 0.751772
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 0.840466
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 0.444057
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 0.942423
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` 0.328655
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 0.200887
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 0.244011
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 0.168425
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 0.373853
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 0.591350
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 0.204211
## `prdline.my.fctriPad 1:cellular.fctr1` 0.575508
## `prdline.my.fctriPad 2:cellular.fctr1` 0.508635
## `prdline.my.fctriPad 3+:cellular.fctr1` 0.485349
## `prdline.my.fctriPadAir:cellular.fctr1` 0.748061
## `prdline.my.fctriPadmini:cellular.fctr1` 0.874783
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 0.195589
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 0.349232
## `prdline.my.fctriPad 2:cellular.fctrUnknown` 0.884376
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 0.709058
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 0.052904
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 0.773341
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 0.697256
##
## (Intercept) ***
## `prdline.my.fctriPad 1` ***
## `prdline.my.fctriPad 2`
## `prdline.my.fctriPad 3+` **
## prdline.my.fctriPadAir ***
## prdline.my.fctriPadmini
## `prdline.my.fctriPadmini 2+` **
## biddable ***
## `condition.fctrFor parts or not working` .
## `condition.fctrManufacturer refurbished`
## condition.fctrNew ***
## `condition.fctrNew other (see details)` *
## `condition.fctrSeller refurbished`
## D.terms.n.post.stop .
## cellular.fctr1
## cellular.fctrUnknown
## `prdline.my.fctrUnknown:.clusterid.fctr2` *
## `prdline.my.fctriPad 1:.clusterid.fctr2`
## `prdline.my.fctriPad 2:.clusterid.fctr2`
## `prdline.my.fctriPad 3+:.clusterid.fctr2`
## `prdline.my.fctriPadAir:.clusterid.fctr2`
## `prdline.my.fctriPadmini:.clusterid.fctr2`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2`
## `prdline.my.fctrUnknown:.clusterid.fctr3`
## `prdline.my.fctriPad 1:.clusterid.fctr3`
## `prdline.my.fctriPad 2:.clusterid.fctr3`
## `prdline.my.fctriPad 3+:.clusterid.fctr3`
## `prdline.my.fctriPadAir:.clusterid.fctr3`
## `prdline.my.fctriPadmini:.clusterid.fctr3`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3`
## `prdline.my.fctrUnknown:.clusterid.fctr4`
## `prdline.my.fctriPad 1:.clusterid.fctr4`
## `prdline.my.fctriPad 2:.clusterid.fctr4`
## `prdline.my.fctriPad 3+:.clusterid.fctr4`
## `prdline.my.fctriPadAir:.clusterid.fctr4`
## `prdline.my.fctriPadmini:.clusterid.fctr4`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4`
## `prdline.my.fctrUnknown:.clusterid.fctr5`
## `prdline.my.fctriPad 1:.clusterid.fctr5`
## `prdline.my.fctriPad 2:.clusterid.fctr5`
## `prdline.my.fctriPad 3+:.clusterid.fctr5`
## `prdline.my.fctriPadAir:.clusterid.fctr5`
## `prdline.my.fctriPadmini:.clusterid.fctr5`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5`
## `prdline.my.fctriPad 1:biddable`
## `prdline.my.fctriPad 2:biddable`
## `prdline.my.fctriPad 3+:biddable` **
## `prdline.my.fctriPadAir:biddable` ***
## `prdline.my.fctriPadmini:biddable` .
## `prdline.my.fctriPadmini 2+:biddable` *
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` .
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` **
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working`
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 1:condition.fctrNew` *
## `prdline.my.fctriPad 2:condition.fctrNew`
## `prdline.my.fctriPad 3+:condition.fctrNew`
## `prdline.my.fctriPadAir:condition.fctrNew` .
## `prdline.my.fctriPadmini:condition.fctrNew`
## `prdline.my.fctriPadmini 2+:condition.fctrNew` .
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)`
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)`
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)`
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished`
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished`
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 1:D.terms.n.post.stop`
## `prdline.my.fctriPad 2:D.terms.n.post.stop`
## `prdline.my.fctriPad 3+:D.terms.n.post.stop`
## `prdline.my.fctriPadAir:D.terms.n.post.stop`
## `prdline.my.fctriPadmini:D.terms.n.post.stop`
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop`
## `prdline.my.fctriPad 1:cellular.fctr1`
## `prdline.my.fctriPad 2:cellular.fctr1`
## `prdline.my.fctriPad 3+:cellular.fctr1`
## `prdline.my.fctriPadAir:cellular.fctr1`
## `prdline.my.fctriPadmini:cellular.fctr1`
## `prdline.my.fctriPadmini 2+:cellular.fctr1`
## `prdline.my.fctriPad 1:cellular.fctrUnknown`
## `prdline.my.fctriPad 2:cellular.fctrUnknown`
## `prdline.my.fctriPad 3+:cellular.fctrUnknown`
## `prdline.my.fctriPadAir:cellular.fctrUnknown` .
## `prdline.my.fctriPadmini:cellular.fctrUnknown`
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 11604.22)
##
## Null deviance: 53948796 on 1858 degrees of freedom
## Residual deviance: 20435032 on 1761 degrees of freedom
## AIC: 22772
##
## Number of Fisher Scoring iterations: 8
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 csm.bayesglm bayesglm
## feats
## 1 prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 2.327 0.656
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB min.aic.fit
## 1 0.6212143 111.174 0.5420843 117.254 22771.55
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.5758072 5.43132 0.04089971
## importance
## biddable 100.000000
## prdline.my.fctr 54.858549
## condition.fctr 46.251715
## D.terms.n.post.stop 3.549158
## .clusterid.fctr 2.906881
## cellular.fctr 0.000000
## [1] "fitting model: csm.glmnet"
## [1] " indep_vars: prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 1.55 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: alpha
## Length Class Mode
## a0 78 -none- numeric
## beta 7566 dgCMatrix S4
## df 78 -none- numeric
## dim 2 -none- numeric
## lambda 78 -none- numeric
## dev.ratio 78 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 97 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 213.9270288
## prdline.my.fctriPad 1
## -88.1079207
## prdline.my.fctriPad 2
## -28.8347259
## prdline.my.fctriPad 3+
## 34.1009207
## prdline.my.fctriPadAir
## 180.4730910
## prdline.my.fctriPadmini 2+
## 44.9583724
## biddable
## -105.9348089
## condition.fctrFor parts or not working
## -52.7205995
## condition.fctrManufacturer refurbished
## 13.4776679
## condition.fctrNew
## 72.5516197
## condition.fctrNew other (see details)
## 56.8526973
## D.terms.n.post.stop
## -0.6632764
## cellular.fctr1
## 22.2024491
## cellular.fctrUnknown
## -6.8389424
## prdline.my.fctrUnknown:.clusterid.fctr2
## 12.8500349
## prdline.my.fctriPad 2:.clusterid.fctr2
## -4.0614250
## prdline.my.fctriPad 3+:.clusterid.fctr2
## 11.2192576
## prdline.my.fctriPadAir:.clusterid.fctr2
## -25.6391677
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 32.3806531
## prdline.my.fctrUnknown:.clusterid.fctr3
## -15.8613765
## prdline.my.fctriPad 2:.clusterid.fctr3
## 14.4681415
## prdline.my.fctriPad 2:.clusterid.fctr4
## -23.2787506
## prdline.my.fctriPadmini:.clusterid.fctr5
## 16.4820066
## prdline.my.fctriPad 1:biddable
## 26.7493911
## prdline.my.fctriPad 3+:biddable
## -15.8995711
## prdline.my.fctriPadAir:biddable
## -94.6969257
## prdline.my.fctriPadmini:biddable
## -1.6890910
## prdline.my.fctriPad 3+:condition.fctrFor parts or not working
## -34.6485868
## prdline.my.fctriPadAir:condition.fctrFor parts or not working
## -68.7000594
## prdline.my.fctriPadmini:condition.fctrFor parts or not working
## -21.2351246
## prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working
## -30.4153588
## prdline.my.fctriPad 1:condition.fctrManufacturer refurbished
## -13.9963780
## prdline.my.fctriPadAir:condition.fctrManufacturer refurbished
## 7.4687533
## prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished
## 28.4716635
## prdline.my.fctriPad 1:condition.fctrNew
## 62.4498443
## prdline.my.fctriPad 2:condition.fctrNew
## -113.4874297
## prdline.my.fctriPad 3+:condition.fctrNew
## 53.4901520
## prdline.my.fctriPadAir:condition.fctrNew
## 63.3443066
## prdline.my.fctriPadmini 2+:condition.fctrNew
## 64.3137222
## prdline.my.fctriPad 1:condition.fctrNew other (see details)
## -19.4268125
## prdline.my.fctriPad 3+:condition.fctrNew other (see details)
## 33.7406673
## prdline.my.fctriPadmini:condition.fctrNew other (see details)
## -1.3658391
## prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)
## 37.0507957
## prdline.my.fctriPad 1:condition.fctrSeller refurbished
## -17.2848801
## prdline.my.fctriPadAir:condition.fctrSeller refurbished
## -7.3556611
## prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished
## -11.4951984
## prdline.my.fctriPad 3+:D.terms.n.post.stop
## 0.4729343
## prdline.my.fctriPadAir:D.terms.n.post.stop
## -0.2499588
## prdline.my.fctriPadAir:cellular.fctr1
## 28.6844203
## prdline.my.fctriPadmini:cellular.fctr1
## 13.9060696
## prdline.my.fctriPadmini 2+:cellular.fctr1
## 62.2744278
## prdline.my.fctriPad 1:cellular.fctrUnknown
## 14.1913278
## prdline.my.fctriPadAir:cellular.fctrUnknown
## -52.1338431
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 199.9327019
## prdline.my.fctriPad 1
## -95.3213996
## prdline.my.fctriPad 2
## -17.6544477
## prdline.my.fctriPad 3+
## 61.2766116
## prdline.my.fctriPadAir
## 211.3109580
## prdline.my.fctriPadmini
## 16.5598709
## prdline.my.fctriPadmini 2+
## 67.0833139
## biddable
## -75.4223453
## condition.fctrFor parts or not working
## -45.5700066
## condition.fctrManufacturer refurbished
## 10.4962592
## condition.fctrNew
## 83.0249700
## condition.fctrNew other (see details)
## 98.8530343
## D.terms.n.post.stop
## -3.6450050
## cellular.fctr1
## 27.2815962
## cellular.fctrUnknown
## -16.6631315
## prdline.my.fctrUnknown:.clusterid.fctr2
## 52.6462946
## prdline.my.fctriPad 1:.clusterid.fctr2
## -4.1592830
## prdline.my.fctriPad 2:.clusterid.fctr2
## -2.4735396
## prdline.my.fctriPad 3+:.clusterid.fctr2
## 17.1371918
## prdline.my.fctriPadAir:.clusterid.fctr2
## -31.4026811
## prdline.my.fctriPadmini:.clusterid.fctr2
## 12.6380714
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 29.9292812
## prdline.my.fctrUnknown:.clusterid.fctr3
## -7.3267386
## prdline.my.fctriPad 1:.clusterid.fctr3
## 6.7742158
## prdline.my.fctriPad 2:.clusterid.fctr3
## 38.0617860
## prdline.my.fctriPad 3+:.clusterid.fctr3
## -4.5633329
## prdline.my.fctriPadAir:.clusterid.fctr3
## -4.8086352
## prdline.my.fctriPadmini:.clusterid.fctr3
## 6.1738158
## prdline.my.fctriPadmini 2+:.clusterid.fctr3
## -26.7391910
## prdline.my.fctriPad 1:.clusterid.fctr4
## 14.1456150
## prdline.my.fctriPad 2:.clusterid.fctr4
## -30.0063010
## prdline.my.fctriPad 3+:.clusterid.fctr4
## 7.1332797
## prdline.my.fctriPadAir:.clusterid.fctr4
## 10.6831383
## prdline.my.fctriPadmini:.clusterid.fctr4
## 21.4468501
## prdline.my.fctriPad 2:.clusterid.fctr5
## 20.2123257
## prdline.my.fctriPadmini:.clusterid.fctr5
## 51.3616246
## prdline.my.fctriPad 1:biddable
## 18.7956345
## prdline.my.fctriPad 2:biddable
## -30.8476585
## prdline.my.fctriPad 3+:biddable
## -61.3313985
## prdline.my.fctriPadAir:biddable
## -139.6559331
## prdline.my.fctriPadmini:biddable
## -36.1780367
## prdline.my.fctriPadmini 2+:biddable
## -48.2891177
## prdline.my.fctriPad 1:condition.fctrFor parts or not working
## 8.7984975
## prdline.my.fctriPad 3+:condition.fctrFor parts or not working
## -54.2990824
## prdline.my.fctriPadAir:condition.fctrFor parts or not working
## -91.7069067
## prdline.my.fctriPadmini:condition.fctrFor parts or not working
## -37.1385439
## prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working
## -66.6446874
## prdline.my.fctriPad 1:condition.fctrManufacturer refurbished
## -77.7449988
## prdline.my.fctriPad 2:condition.fctrManufacturer refurbished
## 20.1713469
## prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished
## 0.7814553
## prdline.my.fctriPadAir:condition.fctrManufacturer refurbished
## 22.1684708
## prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished
## 56.5117821
## prdline.my.fctriPad 1:condition.fctrNew
## 94.2074837
## prdline.my.fctriPad 2:condition.fctrNew
## -176.0478546
## prdline.my.fctriPad 3+:condition.fctrNew
## 59.5783787
## prdline.my.fctriPadAir:condition.fctrNew
## 45.9984402
## prdline.my.fctriPadmini:condition.fctrNew
## -23.3821225
## prdline.my.fctriPadmini 2+:condition.fctrNew
## 54.0834000
## prdline.my.fctriPad 1:condition.fctrNew other (see details)
## -84.9585510
## prdline.my.fctriPad 2:condition.fctrNew other (see details)
## -34.4458692
## prdline.my.fctriPadAir:condition.fctrNew other (see details)
## -43.8274786
## prdline.my.fctriPadmini:condition.fctrNew other (see details)
## -72.0346734
## prdline.my.fctriPad 1:condition.fctrSeller refurbished
## -26.9383264
## prdline.my.fctriPad 2:condition.fctrSeller refurbished
## -5.8314594
## prdline.my.fctriPadAir:condition.fctrSeller refurbished
## -26.2387464
## prdline.my.fctriPadmini:condition.fctrSeller refurbished
## 11.0916535
## prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished
## -50.7832328
## prdline.my.fctriPad 1:D.terms.n.post.stop
## 3.3822120
## prdline.my.fctriPad 2:D.terms.n.post.stop
## 2.2701179
## prdline.my.fctriPad 3+:D.terms.n.post.stop
## 3.0702341
## prdline.my.fctriPadAir:D.terms.n.post.stop
## 1.1799476
## prdline.my.fctriPadmini:D.terms.n.post.stop
## 0.2843634
## prdline.my.fctriPadmini 2+:D.terms.n.post.stop
## 4.5940713
## prdline.my.fctriPad 1:cellular.fctr1
## -4.4916057
## prdline.my.fctriPad 2:cellular.fctr1
## -7.9154685
## prdline.my.fctriPad 3+:cellular.fctr1
## -8.5965368
## prdline.my.fctriPadAir:cellular.fctr1
## 27.7474662
## prdline.my.fctriPadmini:cellular.fctr1
## 22.1497692
## prdline.my.fctriPadmini 2+:cellular.fctr1
## 65.5832361
## prdline.my.fctriPad 1:cellular.fctrUnknown
## 43.9075441
## prdline.my.fctriPad 3+:cellular.fctrUnknown
## -5.4946802
## prdline.my.fctriPadAir:cellular.fctrUnknown
## -61.3772396
## prdline.my.fctriPadmini:cellular.fctrUnknown
## 15.8078887
## prdline.my.fctriPadmini 2+:cellular.fctrUnknown
## 19.7417349
## character(0)
## character(0)
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 csm.glmnet glmnet
## feats
## 1 prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 9 1.36 0.028
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.6135158 109.7313 0.5437791 117.0369 0.5851913
## min.RMSESD.fit max.RsquaredSD.fit
## 1 5.255599 0.0412464
## importance
## prdline.my.fctriPadAir 100.00000
## condition.fctrNew 63.29338
## prdline.my.fctriPadmini 2+:condition.fctrNew 60.51712
## prdline.my.fctriPadAir:condition.fctrNew 60.16864
## prdline.my.fctriPad 1:condition.fctrNew 59.93368
## prdline.my.fctriPadmini 2+:cellular.fctr1 59.82110
## [1] "fitting model: csm.rpart"
## [1] " indep_vars: prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.052 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 1859
##
## CP nsplit rel error
## 1 0.20823484 0 1.0000000
## 2 0.15083018 1 0.7917652
## 3 0.05203829 2 0.6409350
##
## Variable importance
## biddable
## 29
## prdline.my.fctriPadAir
## 21
## prdline.my.fctriPadAir:D.terms.n.post.stop
## 8
## prdline.my.fctriPadAir:cellular.fctr1
## 7
## prdline.my.fctriPadAir:condition.fctrNew
## 7
## prdline.my.fctriPad 3+:biddable
## 5
## prdline.my.fctriPadAir:biddable
## 5
## prdline.my.fctriPad 2:biddable
## 5
## prdline.my.fctriPadmini:biddable
## 5
## prdline.my.fctriPad 1:biddable
## 4
## prdline.my.fctriPadAir:.clusterid.fctr2
## 3
## prdline.my.fctriPadAir:.clusterid.fctr3
## 2
##
## Node number 1: 1859 observations, complexity param=0.2082348
## mean=211.3404, MSE=29020.33
## left son=2 (837 obs) right son=3 (1022 obs)
## Primary splits:
## biddable < 0.5 to the right, improve=0.20823480, (0 missing)
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.19258840, (0 missing)
## condition.fctrNew < 0.5 to the left, improve=0.18855550, (0 missing)
## prdline.my.fctriPadAir:condition.fctrNew < 0.5 to the left, improve=0.15606810, (0 missing)
## prdline.my.fctriPadAir:cellular.fctr1 < 0.5 to the left, improve=0.08504405, (0 missing)
## Surrogate splits:
## prdline.my.fctriPad 3+:biddable < 0.5 to the right, agree=0.626, adj=0.170, (0 split)
## prdline.my.fctriPadAir:biddable < 0.5 to the right, agree=0.626, adj=0.168, (0 split)
## prdline.my.fctriPad 2:biddable < 0.5 to the right, agree=0.625, adj=0.167, (0 split)
## prdline.my.fctriPadmini:biddable < 0.5 to the right, agree=0.623, adj=0.162, (0 split)
## prdline.my.fctriPad 1:biddable < 0.5 to the right, agree=0.613, adj=0.141, (0 split)
##
## Node number 2: 837 observations
## mean=125.4409, MSE=18411.58
##
## Node number 3: 1022 observations, complexity param=0.1508302
## mean=281.6905, MSE=26716.52
## left son=6 (810 obs) right son=7 (212 obs)
## Primary splits:
## prdline.my.fctriPadAir < 0.5 to the left, improve=0.2980157, (0 missing)
## prdline.my.fctriPadAir:condition.fctrNew < 0.5 to the left, improve=0.2071736, (0 missing)
## condition.fctrNew < 0.5 to the left, improve=0.1763299, (0 missing)
## prdline.my.fctriPadAir:cellular.fctr1 < 0.5 to the left, improve=0.1499817, (0 missing)
## prdline.my.fctriPad 1 < 0.5 to the right, improve=0.1189930, (0 missing)
## Surrogate splits:
## prdline.my.fctriPadAir:D.terms.n.post.stop < 1 to the left, agree=0.871, adj=0.377, (0 split)
## prdline.my.fctriPadAir:cellular.fctr1 < 0.5 to the left, agree=0.864, adj=0.344, (0 split)
## prdline.my.fctriPadAir:condition.fctrNew < 0.5 to the left, agree=0.862, adj=0.335, (0 split)
## prdline.my.fctriPadAir:.clusterid.fctr2 < 0.5 to the left, agree=0.819, adj=0.127, (0 split)
## prdline.my.fctriPadAir:.clusterid.fctr3 < 0.5 to the left, agree=0.809, adj=0.080, (0 split)
##
## Node number 6: 810 observations
## mean=236.0411, MSE=17881.31
##
## Node number 7: 212 observations
## mean=456.1057, MSE=22091.13
##
## n= 1859
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 1859 53948800 211.3404
## 2) biddable>=0.5 837 15410490 125.4409 *
## 3) biddable< 0.5 1022 27304290 281.6905
## 6) prdline.my.fctriPadAir< 0.5 810 14483860 236.0411 *
## 7) prdline.my.fctriPadAir>=0.5 212 4683319 456.1057 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 csm.rpart rpart
## feats
## 1 prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 1.966 0.112
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.359065 133.2986 0.3209143 142.7899 0.3897786
## min.RMSESD.fit max.RsquaredSD.fit
## 1 2.725981 0.0302287
## importance
## prdline.my.fctriPadAir 100.00000
## condition.fctrNew 74.37472
## prdline.my.fctriPadAir:condition.fctrNew 74.03967
## prdline.my.fctriPadAir:cellular.fctr1 47.90539
## biddable 42.44458
## prdline.my.fctriPad 1 24.25439
## [1] "fitting model: csm.rf"
## [1] " indep_vars: prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 49 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 1859 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 1859 -none- numeric
## importance 97 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 1859 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 97 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 csm.rf rf
## feats
## 1 prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 61.304 21.416
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.7175522 110.4147 0.5391101 117.6358 0.5822801
## min.RMSESD.fit max.RsquaredSD.fit
## 1 6.140705 0.04740071
## importance
## biddable 100.00000
## prdline.my.fctriPadAir 71.59472
## condition.fctrNew 52.09600
## prdline.my.fctriPadAir:condition.fctrNew 22.90645
## prdline.my.fctriPadmini 2+ 15.53986
## prdline.my.fctriPad 1 14.94164# Ntv.1.lm <- lm(reformulate(indep_vars_vctr, glb_rsp_var), glb_trnobs_df); print(summary(Ntv.1.lm))
#print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])
#csm_featsimp_df[grepl("H.npnct19.log", row.names(csm_featsimp_df)), , FALSE]
#csm_OOBobs_df <- glb_get_predictions(glb_OOBobs_df, mdl_id=csm_mdl_id, rsp_var_out=glb_rsp_var_out, prob_threshold_def=glb_models_df[glb_models_df$model_id == csm_mdl_id, "opt.prob.threshold.OOB"])
#print(sprintf("%s OOB confusion matrix & accuracy: ", csm_mdl_id)); print(t(confusionMatrix(csm_OOBobs_df[, paste0(glb_rsp_var_out, csm_mdl_id)], csm_OOBobs_df[, glb_rsp_var])$table))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$importance)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$importance)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
#print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id)); print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)], glb_OOBobs_df[, glb_rsp_var])$table))
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glb_fitobs_df),
# union(glb_rsp_var, glb_exclude_vars_as_features)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(model_id=paste0(model_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glb_model_metric_terms,
# model_summaryFunction=glb_model_metric_smmry,
# model_metric=glb_model_metric,
# model_metric_maximize=glb_model_metric_maximize)
# Simplify a model
# fit_df <- glb_fitobs_df; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glb_fitobs_df, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glb_model_metric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## model_id model_method
## MFO.lm MFO.lm lm
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.lm Max.cor.Y.lm lm
## Interact.High.cor.Y.lm Interact.High.cor.Y.lm lm
## Low.cor.X.lm Low.cor.X.lm lm
## All.X.lm All.X.lm lm
## All.X.glm All.X.glm glm
## All.X.bayesglm All.X.bayesglm bayesglm
## All.X.glmnet All.X.glmnet glmnet
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf rf
## All.Interact.X.lm All.Interact.X.lm lm
## All.Interact.X.glm All.Interact.X.glm glm
## All.Interact.X.bayesglm All.Interact.X.bayesglm bayesglm
## All.Interact.X.glmnet All.Interact.X.glmnet glmnet
## All.Interact.X.no.rnorm.rpart All.Interact.X.no.rnorm.rpart rpart
## All.Interact.X.no.rnorm.rf All.Interact.X.no.rnorm.rf rf
## csm.lm csm.lm lm
## csm.glm csm.glm glm
## csm.bayesglm csm.bayesglm bayesglm
## csm.glmnet csm.glmnet glmnet
## csm.rpart csm.rpart rpart
## csm.rf csm.rf rf
## feats
## MFO.lm .rnorm
## Max.cor.Y.cv.0.rpart biddable, prdline.my.fctr
## Max.cor.Y.cv.0.cp.0.rpart biddable, prdline.my.fctr
## Max.cor.Y.rpart biddable, prdline.my.fctr
## Max.cor.Y.lm biddable, prdline.my.fctr
## Interact.High.cor.Y.lm biddable, prdline.my.fctr, biddable:D.npnct24.log, biddable:carrier.fctr, biddable:D.npnct06.log, biddable:D.terms.n.post.stop, biddable:D.nstopwrds.log, biddable:D.TfIdf.sum.post.stop, biddable:D.terms.n.post.stop.log, biddable:D.nwrds.unq.log, biddable:D.nuppr.log, biddable:D.TfIdf.sum.post.stem
## Low.cor.X.lm prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.ratio.sum.TfIdf.nwrds, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.lm prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.glm prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.bayesglm prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.glmnet prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.no.rnorm.rpart prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.no.rnorm.rf prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.lm prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.glm prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.bayesglm prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.glmnet prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.no.rnorm.rpart prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.no.rnorm.rf prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## csm.lm prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.glm prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.bayesglm prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.glmnet prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.rpart prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.rf prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything
## MFO.lm 0 0.486
## Max.cor.Y.cv.0.rpart 0 0.635
## Max.cor.Y.cv.0.cp.0.rpart 0 0.478
## Max.cor.Y.rpart 3 1.025
## Max.cor.Y.lm 1 0.966
## Interact.High.cor.Y.lm 1 0.956
## Low.cor.X.lm 1 1.283
## All.X.lm 1 1.196
## All.X.glm 1 1.302
## All.X.bayesglm 1 2.998
## All.X.glmnet 9 1.457
## All.X.no.rnorm.rpart 3 1.821
## All.X.no.rnorm.rf 3 68.482
## All.Interact.X.lm 1 1.334
## All.Interact.X.glm 1 1.298
## All.Interact.X.bayesglm 1 2.999
## All.Interact.X.glmnet 9 1.480
## All.Interact.X.no.rnorm.rpart 3 2.168
## All.Interact.X.no.rnorm.rf 3 81.283
## csm.lm 1 1.269
## csm.glm 1 1.349
## csm.bayesglm 1 2.327
## csm.glmnet 9 1.360
## csm.rpart 3 1.966
## csm.rf 3 61.304
## min.elapsedtime.final max.R.sq.fit
## MFO.lm 0.004 0.000059178
## Max.cor.Y.cv.0.rpart 0.015 0.000000000
## Max.cor.Y.cv.0.cp.0.rpart 0.011 0.505945116
## Max.cor.Y.rpart 0.015 0.359065023
## Max.cor.Y.lm 0.006 0.484368211
## Interact.High.cor.Y.lm 0.014 0.496495648
## Low.cor.X.lm 0.053 0.664882329
## All.X.lm 0.063 0.669901170
## All.X.glm 0.102 0.669901170
## All.X.bayesglm 0.745 0.669726901
## All.X.glmnet 0.040 0.664798108
## All.X.no.rnorm.rpart 0.126 0.359065023
## All.X.no.rnorm.rf 24.206 0.923864443
## All.Interact.X.lm 0.101 0.690917027
## All.Interact.X.glm 0.116 0.690917027
## All.Interact.X.bayesglm 0.982 0.690710407
## All.Interact.X.glmnet 0.049 0.688556502
## All.Interact.X.no.rnorm.rpart 0.128 0.361093426
## All.Interact.X.no.rnorm.rf 28.481 0.927637864
## csm.lm 0.073 0.621216916
## csm.glm 0.120 0.621216916
## csm.bayesglm 0.656 0.621214303
## csm.glmnet 0.028 0.613515794
## csm.rpart 0.112 0.359065023
## csm.rf 21.416 0.717552167
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB
## MFO.lm 170.34851 -0.0009216371 173.3545
## Max.cor.Y.cv.0.rpart 170.35355 0.0000000000 173.2747
## Max.cor.Y.cv.0.cp.0.rpart 119.73987 0.4508173959 128.4084
## Max.cor.Y.rpart 134.81838 0.3209142767 142.7899
## Max.cor.Y.lm 122.75585 0.4300359559 130.8154
## Interact.High.cor.Y.lm 122.17614 0.4278967451 131.0606
## Low.cor.X.lm 102.63086 0.5499382378 116.2442
## All.X.lm 102.19677 0.5509503685 116.1134
## All.X.glm 102.19677 0.5509503685 116.1134
## All.X.bayesglm 102.13102 0.5514321729 116.0511
## All.X.glmnet 101.89882 0.5586710646 115.1109
## All.X.no.rnorm.rpart 133.29864 0.3209142767 142.7899
## All.X.no.rnorm.rf 97.56147 0.5474903751 116.5649
## All.Interact.X.lm 99.41512 0.5271413171 119.1518
## All.Interact.X.glm 99.41512 0.5271413171 119.1518
## All.Interact.X.bayesglm 99.36220 0.5274745018 119.1099
## All.Interact.X.glmnet 99.25138 0.5363701270 117.9834
## All.Interact.X.no.rnorm.rpart 133.52978 0.3212929245 142.7501
## All.Interact.X.no.rnorm.rf 99.81446 0.5301107517 118.7819
## csm.lm 111.25921 0.5420391989 117.2598
## csm.glm 111.25921 0.5420391989 117.2598
## csm.bayesglm 111.17402 0.5420843077 117.2540
## csm.glmnet 109.73134 0.5437790872 117.0369
## csm.rpart 133.29864 0.3209142767 142.7899
## csm.rf 110.41473 0.5391101087 117.6358
## max.Adj.R.sq.fit max.Rsquared.fit
## MFO.lm -0.0004792931 NA
## Max.cor.Y.cv.0.rpart NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA
## Max.cor.Y.rpart NA 0.3757515
## Max.cor.Y.lm 0.4824182255 0.4808090
## Interact.High.cor.Y.lm 0.4904623716 0.4857043
## Low.cor.X.lm 0.6523458225 0.6367757
## All.X.lm 0.6550485788 0.6397998
## All.X.glm NA 0.6397998
## All.X.bayesglm NA 0.6402516
## All.X.glmnet NA 0.6414783
## All.X.no.rnorm.rpart NA 0.3897786
## All.X.no.rnorm.rf NA 0.6705374
## All.Interact.X.lm 0.6748153092 0.6592594
## All.Interact.X.glm NA 0.6592594
## All.Interact.X.bayesglm NA 0.6595932
## All.Interact.X.glmnet NA 0.6600662
## All.Interact.X.no.rnorm.rpart NA 0.3886849
## All.Interact.X.no.rnorm.rf NA 0.6555804
## csm.lm 0.6019349714 0.5752187
## csm.glm NA 0.5752187
## csm.bayesglm NA 0.5758072
## csm.glmnet NA 0.5851913
## csm.rpart NA 0.3897786
## csm.rf NA 0.5822801
## min.RMSESD.fit max.RsquaredSD.fit
## MFO.lm NA NA
## Max.cor.Y.cv.0.rpart NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA
## Max.cor.Y.rpart 2.629090 0.02583973
## Max.cor.Y.lm 3.627481 0.03270880
## Interact.High.cor.Y.lm 4.465793 0.03984517
## Low.cor.X.lm 5.230233 0.03879193
## All.X.lm 5.770372 0.04332778
## All.X.glm 5.770372 0.04332778
## All.X.bayesglm 5.646925 0.04237281
## All.X.glmnet 5.733788 0.04264401
## All.X.no.rnorm.rpart 2.725981 0.03022870
## All.X.no.rnorm.rf 8.181706 0.05684180
## All.Interact.X.lm 5.921147 0.04305645
## All.Interact.X.glm 5.921147 0.04305645
## All.Interact.X.bayesglm 5.855718 0.04250045
## All.Interact.X.glmnet 5.858388 0.04223710
## All.Interact.X.no.rnorm.rpart 3.122861 0.03323354
## All.Interact.X.no.rnorm.rf 7.629279 0.05368373
## csm.lm 5.391855 0.04061329
## csm.glm 5.391855 0.04061329
## csm.bayesglm 5.431320 0.04089971
## csm.glmnet 5.255599 0.04124640
## csm.rpart 2.725981 0.03022870
## csm.rf 6.140705 0.04740071
## min.aic.fit
## MFO.lm NA
## Max.cor.Y.cv.0.rpart NA
## Max.cor.Y.cv.0.cp.0.rpart NA
## Max.cor.Y.rpart NA
## Max.cor.Y.lm NA
## Interact.High.cor.Y.lm NA
## Low.cor.X.lm NA
## All.X.lm NA
## All.X.glm 22481.79
## All.X.bayesglm 22502.77
## All.X.glmnet NA
## All.X.no.rnorm.rpart NA
## All.X.no.rnorm.rf NA
## All.Interact.X.lm NA
## All.Interact.X.glm 22383.50
## All.Interact.X.bayesglm 22404.74
## All.Interact.X.glmnet NA
## All.Interact.X.no.rnorm.rpart NA
## All.Interact.X.no.rnorm.rf NA
## csm.lm NA
## csm.glm 22757.53
## csm.bayesglm 22771.55
## csm.glmnet NA
## csm.rpart NA
## csm.rf NArm(ret_lst)
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 13 fit.models_1_rf 13 0 225.603 391.688 166.085
## 14 fit.models_1_end 14 0 391.689 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 114.154 391.695 277.541
## 12 fit.models 7 2 391.695 NA NAif (!is.null(glb_model_metric_smmry)) {
stats_df <- glb_models_df[, "model_id", FALSE]
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_fitobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "fit",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_OOBobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "OOB",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
print("Merging following data into glb_models_df:")
print(stats_mrg_df <- stats_df[, c(1, grep(glb_model_metric, names(stats_df)))])
print(tmp_models_df <- orderBy(~model_id, glb_models_df[, c("model_id",
grep(glb_model_metric, names(stats_df), value=TRUE))]))
tmp2_models_df <- glb_models_df[, c("model_id", setdiff(names(glb_models_df),
grep(glb_model_metric, names(stats_df), value=TRUE)))]
tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
print(tmp3_models_df)
print(names(tmp3_models_df))
print(glb_models_df <- subset(tmp3_models_df, select=-model_id.1))
}
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## model_id model_method
## MFO.lm MFO.lm lm
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.lm Max.cor.Y.lm lm
## Interact.High.cor.Y.lm Interact.High.cor.Y.lm lm
## Low.cor.X.lm Low.cor.X.lm lm
## All.X.lm All.X.lm lm
## All.X.glm All.X.glm glm
## All.X.bayesglm All.X.bayesglm bayesglm
## All.X.glmnet All.X.glmnet glmnet
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf rf
## All.Interact.X.lm All.Interact.X.lm lm
## All.Interact.X.glm All.Interact.X.glm glm
## All.Interact.X.bayesglm All.Interact.X.bayesglm bayesglm
## All.Interact.X.glmnet All.Interact.X.glmnet glmnet
## All.Interact.X.no.rnorm.rpart All.Interact.X.no.rnorm.rpart rpart
## All.Interact.X.no.rnorm.rf All.Interact.X.no.rnorm.rf rf
## csm.lm csm.lm lm
## csm.glm csm.glm glm
## csm.bayesglm csm.bayesglm bayesglm
## csm.glmnet csm.glmnet glmnet
## csm.rpart csm.rpart rpart
## csm.rf csm.rf rf
## feats
## MFO.lm .rnorm
## Max.cor.Y.cv.0.rpart biddable, prdline.my.fctr
## Max.cor.Y.cv.0.cp.0.rpart biddable, prdline.my.fctr
## Max.cor.Y.rpart biddable, prdline.my.fctr
## Max.cor.Y.lm biddable, prdline.my.fctr
## Interact.High.cor.Y.lm biddable, prdline.my.fctr, biddable:D.npnct24.log, biddable:carrier.fctr, biddable:D.npnct06.log, biddable:D.terms.n.post.stop, biddable:D.nstopwrds.log, biddable:D.TfIdf.sum.post.stop, biddable:D.terms.n.post.stop.log, biddable:D.nwrds.unq.log, biddable:D.nuppr.log, biddable:D.TfIdf.sum.post.stem
## Low.cor.X.lm prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.ratio.sum.TfIdf.nwrds, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.lm prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.glm prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.bayesglm prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.glmnet prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.no.rnorm.rpart prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr
## All.X.no.rnorm.rf prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.lm prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.glm prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.bayesglm prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.glmnet prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.no.rnorm.rpart prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.no.rnorm.rf prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, prdline.my.fctr*biddable, prdline.my.fctr*idseq.my, prdline.my.fctr:.clusterid.fctr
## csm.lm prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.glm prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.bayesglm prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.glmnet prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.rpart prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.rf prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns max.R.sq.fit max.R.sq.OOB
## MFO.lm 0 0.000059178 -0.0009216371
## Max.cor.Y.cv.0.rpart 0 0.000000000 0.0000000000
## Max.cor.Y.cv.0.cp.0.rpart 0 0.505945116 0.4508173959
## Max.cor.Y.rpart 3 0.359065023 0.3209142767
## Max.cor.Y.lm 1 0.484368211 0.4300359559
## Interact.High.cor.Y.lm 1 0.496495648 0.4278967451
## Low.cor.X.lm 1 0.664882329 0.5499382378
## All.X.lm 1 0.669901170 0.5509503685
## All.X.glm 1 0.669901170 0.5509503685
## All.X.bayesglm 1 0.669726901 0.5514321729
## All.X.glmnet 9 0.664798108 0.5586710646
## All.X.no.rnorm.rpart 3 0.359065023 0.3209142767
## All.X.no.rnorm.rf 3 0.923864443 0.5474903751
## All.Interact.X.lm 1 0.690917027 0.5271413171
## All.Interact.X.glm 1 0.690917027 0.5271413171
## All.Interact.X.bayesglm 1 0.690710407 0.5274745018
## All.Interact.X.glmnet 9 0.688556502 0.5363701270
## All.Interact.X.no.rnorm.rpart 3 0.361093426 0.3212929245
## All.Interact.X.no.rnorm.rf 3 0.927637864 0.5301107517
## csm.lm 1 0.621216916 0.5420391989
## csm.glm 1 0.621216916 0.5420391989
## csm.bayesglm 1 0.621214303 0.5420843077
## csm.glmnet 9 0.613515794 0.5437790872
## csm.rpart 3 0.359065023 0.3209142767
## csm.rf 3 0.717552167 0.5391101087
## max.Adj.R.sq.fit max.Rsquared.fit
## MFO.lm -0.0004792931 NA
## Max.cor.Y.cv.0.rpart NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA
## Max.cor.Y.rpart NA 0.3757515
## Max.cor.Y.lm 0.4824182255 0.4808090
## Interact.High.cor.Y.lm 0.4904623716 0.4857043
## Low.cor.X.lm 0.6523458225 0.6367757
## All.X.lm 0.6550485788 0.6397998
## All.X.glm NA 0.6397998
## All.X.bayesglm NA 0.6402516
## All.X.glmnet NA 0.6414783
## All.X.no.rnorm.rpart NA 0.3897786
## All.X.no.rnorm.rf NA 0.6705374
## All.Interact.X.lm 0.6748153092 0.6592594
## All.Interact.X.glm NA 0.6592594
## All.Interact.X.bayesglm NA 0.6595932
## All.Interact.X.glmnet NA 0.6600662
## All.Interact.X.no.rnorm.rpart NA 0.3886849
## All.Interact.X.no.rnorm.rf NA 0.6555804
## csm.lm 0.6019349714 0.5752187
## csm.glm NA 0.5752187
## csm.bayesglm NA 0.5758072
## csm.glmnet NA 0.5851913
## csm.rpart NA 0.3897786
## csm.rf NA 0.5822801
## inv.elapsedtime.everything
## MFO.lm 2.05761317
## Max.cor.Y.cv.0.rpart 1.57480315
## Max.cor.Y.cv.0.cp.0.rpart 2.09205021
## Max.cor.Y.rpart 0.97560976
## Max.cor.Y.lm 1.03519669
## Interact.High.cor.Y.lm 1.04602510
## Low.cor.X.lm 0.77942323
## All.X.lm 0.83612040
## All.X.glm 0.76804916
## All.X.bayesglm 0.33355570
## All.X.glmnet 0.68634180
## All.X.no.rnorm.rpart 0.54914882
## All.X.no.rnorm.rf 0.01460238
## All.Interact.X.lm 0.74962519
## All.Interact.X.glm 0.77041602
## All.Interact.X.bayesglm 0.33344448
## All.Interact.X.glmnet 0.67567568
## All.Interact.X.no.rnorm.rpart 0.46125461
## All.Interact.X.no.rnorm.rf 0.01230270
## csm.lm 0.78802206
## csm.glm 0.74128984
## csm.bayesglm 0.42973786
## csm.glmnet 0.73529412
## csm.rpart 0.50864700
## csm.rf 0.01631215
## inv.elapsedtime.final inv.RMSE.fit
## MFO.lm 250.00000000 0.005870319
## Max.cor.Y.cv.0.rpart 66.66666667 0.005870145
## Max.cor.Y.cv.0.cp.0.rpart 90.90909091 0.008351437
## Max.cor.Y.rpart 66.66666667 0.007417386
## Max.cor.Y.lm 166.66666667 0.008146252
## Interact.High.cor.Y.lm 71.42857143 0.008184904
## Low.cor.X.lm 18.86792453 0.009743658
## All.X.lm 15.87301587 0.009785045
## All.X.glm 9.80392157 0.009785045
## All.X.bayesglm 1.34228188 0.009791344
## All.X.glmnet 25.00000000 0.009813656
## All.X.no.rnorm.rpart 7.93650794 0.007501952
## All.X.no.rnorm.rf 0.04131207 0.010249948
## All.Interact.X.lm 9.90099010 0.010058832
## All.Interact.X.glm 8.62068966 0.010058832
## All.Interact.X.bayesglm 1.01832994 0.010064189
## All.Interact.X.glmnet 20.40816327 0.010075427
## All.Interact.X.no.rnorm.rpart 7.81250000 0.007488966
## All.Interact.X.no.rnorm.rf 0.03511113 0.010018588
## csm.lm 13.69863014 0.008988020
## csm.glm 8.33333333 0.008988020
## csm.bayesglm 1.52439024 0.008994907
## csm.glmnet 35.71428571 0.009113167
## csm.rpart 8.92857143 0.007501952
## csm.rf 0.04669406 0.009056762
## inv.RMSE.OOB inv.aic.fit
## MFO.lm 0.005768526 NA
## Max.cor.Y.cv.0.rpart 0.005771184 NA
## Max.cor.Y.cv.0.cp.0.rpart 0.007787652 NA
## Max.cor.Y.rpart 0.007003298 NA
## Max.cor.Y.lm 0.007644361 NA
## Interact.High.cor.Y.lm 0.007630056 NA
## Low.cor.X.lm 0.008602583 NA
## All.X.lm 0.008612272 NA
## All.X.glm 0.008612272 4.448045e-05
## All.X.bayesglm 0.008616896 4.443897e-05
## All.X.glmnet 0.008687278 NA
## All.X.no.rnorm.rpart 0.007003298 NA
## All.X.no.rnorm.rf 0.008578910 NA
## All.Interact.X.lm 0.008392652 NA
## All.Interact.X.glm 0.008392652 4.467577e-05
## All.Interact.X.bayesglm 0.008395611 4.463341e-05
## All.Interact.X.glmnet 0.008475771 NA
## All.Interact.X.no.rnorm.rpart 0.007005251 NA
## All.Interact.X.no.rnorm.rf 0.008418789 NA
## csm.lm 0.008528070 NA
## csm.glm 0.008528070 4.394149e-05
## csm.bayesglm 0.008528490 4.391445e-05
## csm.glmnet 0.008544316 NA
## csm.rpart 0.007003298 NA
## csm.rf 0.008500816 NAprint(myplot_radar(radar_inp_df=plt_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 25. Consider specifying shapes manually if you must have them.## Warning: Removed 5 rows containing missing values (geom_path).## Warning: Removed 203 rows containing missing values (geom_point).## Warning: Removed 40 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 25. Consider specifying shapes manually if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(model_id %in% grep("random|MFO", plt_models_df$model_id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## [1] "var:min.RMSESD.fit"
## [1] "var:max.RsquaredSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "model_id", FALSE]
pltCI_models_df <- glb_models_df[, "model_id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="model_id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="model_id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("model_id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("model_id", "model_method")],
all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
print(gp <- myplot_bar(mltd_models_df, "model_id", "value", colorcol_name="model_method") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=model_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))## Warning: Removed 3 rows containing missing values (position_stack).dev.off()## quartz_off_screen
## 2print(gp)## Warning: Removed 3 rows containing missing values (position_stack).
# used for console inspection
model_evl_terms <- c(NULL)
for (metric in glb_model_evl_criteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse=" "))
dsp_models_cols <- c("model_id", glb_model_evl_criteria)
if (glb_is_classification && glb_is_binomial)
dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])## model_id min.RMSE.OOB max.R.sq.OOB
## 11 All.X.glmnet 115.1109 0.5586710646
## 10 All.X.bayesglm 116.0511 0.5514321729
## 8 All.X.lm 116.1134 0.5509503685
## 9 All.X.glm 116.1134 0.5509503685
## 7 Low.cor.X.lm 116.2442 0.5499382378
## 13 All.X.no.rnorm.rf 116.5649 0.5474903751
## 23 csm.glmnet 117.0369 0.5437790872
## 22 csm.bayesglm 117.2540 0.5420843077
## 20 csm.lm 117.2598 0.5420391989
## 21 csm.glm 117.2598 0.5420391989
## 25 csm.rf 117.6358 0.5391101087
## 17 All.Interact.X.glmnet 117.9834 0.5363701270
## 19 All.Interact.X.no.rnorm.rf 118.7819 0.5301107517
## 16 All.Interact.X.bayesglm 119.1099 0.5274745018
## 14 All.Interact.X.lm 119.1518 0.5271413171
## 15 All.Interact.X.glm 119.1518 0.5271413171
## 3 Max.cor.Y.cv.0.cp.0.rpart 128.4084 0.4508173959
## 5 Max.cor.Y.lm 130.8154 0.4300359559
## 6 Interact.High.cor.Y.lm 131.0606 0.4278967451
## 18 All.Interact.X.no.rnorm.rpart 142.7501 0.3212929245
## 12 All.X.no.rnorm.rpart 142.7899 0.3209142767
## 24 csm.rpart 142.7899 0.3209142767
## 4 Max.cor.Y.rpart 142.7899 0.3209142767
## 2 Max.cor.Y.cv.0.rpart 173.2747 0.0000000000
## 1 MFO.lm 173.3545 -0.0009216371
## max.Adj.R.sq.fit
## 11 NA
## 10 NA
## 8 0.6550485788
## 9 NA
## 7 0.6523458225
## 13 NA
## 23 NA
## 22 NA
## 20 0.6019349714
## 21 NA
## 25 NA
## 17 NA
## 19 NA
## 16 NA
## 14 0.6748153092
## 15 NA
## 3 NA
## 5 0.4824182255
## 6 0.4904623716
## 18 NA
## 12 NA
## 24 NA
## 4 NA
## 2 NA
## 1 -0.0004792931print(myplot_radar(radar_inp_df=dsp_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 25. Consider specifying shapes manually if you must have them.## Warning: Removed 8 rows containing missing values (geom_path).## Warning: Removed 68 rows containing missing values (geom_point).## Warning: Removed 18 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 25. Consider specifying shapes manually if you must have them.
print("Metrics used for model selection:"); print(model_sel_frmla)## [1] "Metrics used for model selection:"## ~+min.RMSE.OOB - max.R.sq.OOB - max.Adj.R.sq.fitprint(sprintf("Best model id: %s", dsp_models_df[1, "model_id"]))## [1] "Best model id: All.X.glmnet"if (is.null(glb_sel_mdl_id)) {
glb_sel_mdl_id <- dsp_models_df[1, "model_id"]
# if (glb_sel_mdl_id == "Interact.High.cor.Y.glm") {
# warning("glb_sel_mdl_id: Interact.High.cor.Y.glm; myextract_mdl_feats does not currently support interaction terms")
# glb_sel_mdl_id <- dsp_models_df[2, "model_id"]
# }
} else
print(sprintf("User specified selection: %s", glb_sel_mdl_id))
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])
## Length Class Mode
## a0 94 -none- numeric
## beta 8460 dgCMatrix S4
## df 94 -none- numeric
## dim 2 -none- numeric
## lambda 94 -none- numeric
## dev.ratio 94 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 90 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 294.78257637
## prdline.my.fctriPad 1
## -76.71045424
## prdline.my.fctriPad 2
## -23.64322090
## prdline.my.fctriPad 3+
## 24.95136901
## prdline.my.fctriPadAir
## 119.79418098
## prdline.my.fctriPadmini 2+
## 57.44456493
## condition.fctrFor parts or not working
## -88.58209077
## condition.fctrManufacturer refurbished
## 20.69027409
## condition.fctrNew
## 90.44265248
## condition.fctrNew other (see details)
## 55.84012240
## condition.fctrSeller refurbished
## -9.35222586
## color.fctrBlack
## -7.46170465
## color.fctrGold
## 50.76406013
## color.fctrSpace Gray
## 6.39766654
## color.fctrWhite
## 6.80464281
## D.TfIdf.sum.stem.stop.Ratio
## 25.43746154
## idseq.my
## 0.02633208
## carrier.fctrSprint
## -16.90344697
## carrier.fctrUnknown
## 30.88068200
## D.npnct09.log
## 71.33981931
## D.npnct10.log
## 32.00751631
## D.terms.n.stem.stop.Ratio
## 27.42161833
## D.npnct28.log
## -31.23070785
## cellular.fctr1
## 14.88145023
## cellular.fctrUnknown
## -49.84674880
## D.npnct14.log
## -6.44241930
## D.npnct05.log
## -11.72906659
## D.npnct08.log
## -6.83561783
## D.npnct01.log
## 13.77599628
## D.npnct15.log
## -8.60514295
## D.npnct11.log
## -7.59188211
## D.npnct03.log
## 21.92795604
## storage.fctr16
## -162.98155887
## storage.fctr32
## -138.23288130
## storage.fctr64
## -94.74980540
## storage.fctrUnknown
## -151.03929961
## D.npnct13.log
## -2.99822888
## D.ratio.sum.TfIdf.nwrds
## -12.14704821
## biddable
## -116.86488285
## prdline.my.fctrUnknown:.clusterid.fctr2
## 26.62965681
## prdline.my.fctriPad 1:.clusterid.fctr2
## -10.22155294
## prdline.my.fctriPad 2:.clusterid.fctr2
## -2.72772819
## prdline.my.fctriPad 3+:.clusterid.fctr2
## 14.53079907
## prdline.my.fctriPadAir:.clusterid.fctr2
## -31.90398968
## prdline.my.fctriPadmini:.clusterid.fctr2
## 0.88956465
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 27.16155447
## prdline.my.fctrUnknown:.clusterid.fctr3
## -5.97461085
## prdline.my.fctriPadAir:.clusterid.fctr3
## -1.17994114
## prdline.my.fctriPadmini 2+:.clusterid.fctr3
## -0.66788627
## prdline.my.fctriPad 1:.clusterid.fctr4
## 11.38256051
## prdline.my.fctriPad 2:.clusterid.fctr4
## -22.79555224
## prdline.my.fctriPad 3+:.clusterid.fctr4
## 2.82723340
## prdline.my.fctriPadmini:.clusterid.fctr5
## 25.38093863
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 1.998775e+02
## prdline.my.fctriPad 1
## -7.644313e+01
## prdline.my.fctriPad 2
## -1.999249e+01
## prdline.my.fctriPad 3+
## 2.914958e+01
## prdline.my.fctriPadAir
## 1.234229e+02
## prdline.my.fctriPadmini
## 3.465888e+00
## prdline.my.fctriPadmini 2+
## 6.086067e+01
## condition.fctrFor parts or not working
## -9.227346e+01
## condition.fctrManufacturer refurbished
## 2.554944e+01
## condition.fctrNew
## 8.687130e+01
## condition.fctrNew other (see details)
## 5.599857e+01
## condition.fctrSeller refurbished
## -1.402468e+01
## color.fctrBlack
## -7.412825e+00
## color.fctrGold
## 5.278243e+01
## color.fctrSpace Gray
## 1.063730e+01
## color.fctrWhite
## 9.549012e+00
## D.TfIdf.sum.stem.stop.Ratio
## 6.591341e+01
## D.ratio.nstopwrds.nwrds
## 2.869129e+01
## idseq.my
## 2.824860e-02
## carrier.fctrOther
## -9.326523e+00
## carrier.fctrSprint
## -3.086106e+01
## carrier.fctrT-Mobile
## -4.973113e+00
## carrier.fctrUnknown
## 3.307679e+01
## carrier.fctrVerizon
## -4.757925e+00
## D.npnct09.log
## 9.151029e+01
## D.npnct10.log
## 5.383163e+01
## D.terms.n.stem.stop.Ratio
## 8.575788e+01
## D.npnct28.log
## -6.770364e+01
## cellular.fctr1
## 1.701378e+01
## cellular.fctrUnknown
## -5.221195e+01
## D.npnct14.log
## -1.732644e+01
## .rnorm
## -2.606380e-01
## D.npnct05.log
## -1.292165e+01
## D.npnct08.log
## -1.823787e+01
## D.npnct01.log
## 1.689144e+01
## D.ndgts.log
## -1.668628e+00
## D.npnct12.log
## 2.053090e+00
## D.npnct16.log
## 1.070520e+01
## D.npnct06.log
## -1.535096e+01
## D.npnct15.log
## -2.019071e+01
## D.npnct11.log
## -1.121243e+01
## D.npnct03.log
## 3.598661e+01
## storage.fctr16
## -2.008292e+02
## storage.fctr32
## -1.783456e+02
## storage.fctr64
## -1.335379e+02
## storage.fctrUnknown
## -1.901398e+02
## D.npnct13.log
## -7.450029e+00
## D.terms.n.post.stop
## -1.798417e+00
## D.ratio.sum.TfIdf.nwrds
## -1.186745e+01
## D.nstopwrds.log
## -2.528831e+01
## D.nwrds.unq.log
## -2.345913e+00
## D.terms.n.post.stem.log
## -1.005925e-02
## D.terms.n.post.stop.log
## -1.337724e-03
## D.nwrds.log
## 4.736143e+01
## D.nchrs.log
## 3.200722e+00
## D.nuppr.log
## -7.287495e+00
## D.npnct24.log
## -5.018553e+01
## D.TfIdf.sum.post.stop
## 1.644332e+00
## biddable
## -1.180910e+02
## prdline.my.fctrUnknown:.clusterid.fctr2
## 4.104686e+01
## prdline.my.fctriPad 1:.clusterid.fctr2
## -8.957112e+00
## prdline.my.fctriPad 2:.clusterid.fctr2
## -1.877347e+00
## prdline.my.fctriPad 3+:.clusterid.fctr2
## 2.133655e+01
## prdline.my.fctriPadAir:.clusterid.fctr2
## -3.824864e+01
## prdline.my.fctriPadmini:.clusterid.fctr2
## 1.142155e+01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 3.398284e+01
## prdline.my.fctrUnknown:.clusterid.fctr3
## -4.613406e+00
## prdline.my.fctriPad 1:.clusterid.fctr3
## 1.277467e+01
## prdline.my.fctriPad 2:.clusterid.fctr3
## 1.452679e+01
## prdline.my.fctriPad 3+:.clusterid.fctr3
## 8.868811e+00
## prdline.my.fctriPadAir:.clusterid.fctr3
## -1.113572e+01
## prdline.my.fctriPadmini:.clusterid.fctr3
## -2.720336e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr3
## -7.412104e+00
## prdline.my.fctriPad 1:.clusterid.fctr4
## 2.954268e+01
## prdline.my.fctriPad 2:.clusterid.fctr4
## -2.510686e+01
## prdline.my.fctriPad 3+:.clusterid.fctr4
## 1.131957e+01
## prdline.my.fctriPadAir:.clusterid.fctr4
## -1.209120e+01
## prdline.my.fctriPadmini:.clusterid.fctr4
## -5.445225e+00
## prdline.my.fctriPad 2:.clusterid.fctr5
## 1.254241e+01
## prdline.my.fctriPadmini:.clusterid.fctr5
## 4.135022e+01
## character(0)
## character(0)## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
glb_get_predictions <- function(df, mdl_id, rsp_var_out, prob_threshold_def=NULL) {
mdl <- glb_models_lst[[mdl_id]]
rsp_var_out <- paste0(rsp_var_out, mdl_id)
if (glb_is_regression) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
print(myplot_scatter(df, glb_rsp_var, rsp_var_out, smooth=TRUE))
df[, paste0(rsp_var_out, ".err")] <-
abs(df[, rsp_var_out] - df[, glb_rsp_var])
print(head(orderBy(reformulate(c("-", paste0(rsp_var_out, ".err"))),
df)))
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$model_id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")[, 2]
df[, rsp_var_out] <-
factor(levels(df[, glb_rsp_var])[
(df[, paste0(rsp_var_out, ".prob")] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# prediction stats already reported by myfit_mdl ???
}
if (glb_is_classification && !glb_is_binomial) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")
}
return(df)
}
glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
rsp_var_out=glb_rsp_var_out)## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
## UniqueID
## 2623 12625
## 2352 12354
## 2100 12102
## 2454 12456
## 2632 12634
## 2094 12096
## description
## 2623 Lot of 10 mixed iPad minis. Colors,models & storage capacity vary between each lot. There may be
## 2352
## 2100
## 2454
## 2632 Good condition IPAD 2 32gb wifi + 3g verizon. LOT OF FIVE.
## 2094
## biddable startprice condition cellular carrier
## 2623 0 999.99 For parts or not working Unknown Unknown
## 2352 1 0.99 Used 0 None
## 2100 1 1.00 New 1 AT&T
## 2454 1 0.99 New 1 T-Mobile
## 2632 0 700.00 Used 1 Verizon
## 2094 0 9.00 Used Unknown Unknown
## color storage productline sold .src .grpid .rnorm idseq.my
## 2623 White Unknown Unknown NA Test <NA> -0.9259777 2625
## 2352 Gold 128 iPad Air 2 NA Test <NA> 0.1545570 2354
## 2100 Space Gray 128 iPad mini 2 NA Test <NA> 2.2673657 2102
## 2454 White 64 iPad Air 2 NA Test <NA> 0.4458716 2456
## 2632 Unknown 32 iPad 2 NA Test <NA> 0.8127608 2634
## 2094 Space Gray 64 iPad Air 2 NA Test <NA> -0.3700640 2096
## prdline.my startprice.log
## 2623 iPadmini 6.90774528
## 2352 iPadAir -0.01005034
## 2100 iPadmini 2+ 0.00000000
## 2454 iPadAir -0.01005034
## 2632 iPad 2 6.55108034
## 2094 iPadAir 2.19722458
## descr.my
## 2623 Lot of 10 mixed iPad minis. Colors, models & storage capacity vary between each lot. There may be
## 2352
## 2100
## 2454
## 2632 Good condition IPAD 2 32gb wifi + 3g verizon. LOT OF FIVE.
## 2094
## condition.fctr cellular.fctr carrier.fctr color.fctr
## 2623 For parts or not working Unknown Unknown White
## 2352 Used 0 None Gold
## 2100 New 1 AT&T Space Gray
## 2454 New 1 T-Mobile White
## 2632 Used 1 Verizon Unknown
## 2094 Used Unknown Unknown Space Gray
## storage.fctr prdline.my.fctr D.terms.n.post.stop
## 2623 Unknown iPadmini 8
## 2352 128 iPadAir 0
## 2100 128 iPadmini 2+ 0
## 2454 64 iPadAir 0
## 2632 32 iPad 2 8
## 2094 64 iPadAir 0
## D.terms.n.post.stop.log D.TfIdf.sum.post.stop D.terms.n.post.stem
## 2623 2.197225 9.127623 8
## 2352 0.000000 0.000000 0
## 2100 0.000000 0.000000 0
## 2454 0.000000 0.000000 0
## 2632 2.197225 7.047501 8
## 2094 0.000000 0.000000 0
## D.terms.n.post.stem.log D.TfIdf.sum.post.stem
## 2623 2.197225 8.069403
## 2352 0.000000 0.000000
## 2100 0.000000 0.000000
## 2454 0.000000 0.000000
## 2632 2.197225 6.969581
## 2094 0.000000 0.000000
## D.terms.n.stem.stop.Ratio D.TfIdf.sum.stem.stop.Ratio D.T.condit
## 2623 1 0.8840640 0.0000000
## 2352 1 1.0000000 0.0000000
## 2100 1 1.0000000 0.0000000
## 2454 1 1.0000000 0.0000000
## 2632 1 0.9889437 0.3026733
## 2094 1 1.0000000 0.0000000
## D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen D.T.great
## 2623 0 0 0 0.0000000 0.3908446 0 0
## 2352 0 0 0 0.0000000 0.0000000 0 0
## 2100 0 0 0 0.0000000 0.0000000 0 0
## 2454 0 0 0 0.0000000 0.0000000 0 0
## 2632 0 0 0 0.4691913 0.4397002 0 0
## 2094 0 0 0 0.0000000 0.0000000 0 0
## D.T.work D.T.excel D.nwrds.log D.nwrds.unq.log D.sum.TfIdf
## 2623 0 0 2.944439 2.197225 8.069403
## 2352 0 0 0.000000 0.000000 0.000000
## 2100 0 0 0.000000 0.000000 0.000000
## 2454 0 0 0.000000 0.000000 0.000000
## 2632 0 0 2.484907 2.197225 6.969581
## 2094 0 0 0.000000 0.000000 0.000000
## D.ratio.sum.TfIdf.nwrds D.nchrs.log D.nuppr.log D.ndgts.log
## 2623 0.4483002 4.634729 4.356709 1.098612
## 2352 0.0000000 0.000000 0.000000 0.000000
## 2100 0.0000000 0.000000 0.000000 0.000000
## 2454 0.0000000 0.000000 0.000000 0.000000
## 2632 0.6335983 4.077537 3.713572 1.609438
## 2094 0.0000000 0.000000 0.000000 0.000000
## D.npnct01.log D.npnct03.log D.npnct05.log D.npnct06.log D.npnct08.log
## 2623 0 0 0 0.6931472 0
## 2352 0 0 0 0.0000000 0
## 2100 0 0 0 0.0000000 0
## 2454 0 0 0 0.0000000 0
## 2632 0 0 0 0.0000000 0
## 2094 0 0 0 0.0000000 0
## D.npnct09.log D.npnct10.log D.npnct11.log D.npnct12.log D.npnct13.log
## 2623 0 0.0000000 0.6931472 0 1.098612
## 2352 0 0.0000000 0.0000000 0 0.000000
## 2100 0 0.0000000 0.0000000 0 0.000000
## 2454 0 0.0000000 0.0000000 0 0.000000
## 2632 0 0.6931472 0.0000000 0 1.098612
## 2094 0 0.0000000 0.0000000 0 0.000000
## D.npnct14.log D.npnct15.log D.npnct16.log D.npnct24.log D.npnct28.log
## 2623 0 0 0.6931472 0.6931472 0
## 2352 0 0 0.0000000 0.0000000 0
## 2100 0 0 0.0000000 0.0000000 0
## 2454 0 0 0.0000000 0.0000000 0
## 2632 0 0 0.0000000 0.6931472 0
## 2094 0 0 0.0000000 0.0000000 0
## D.nstopwrds.log D.ratio.nstopwrds.nwrds D.P.mini D.P.air .clusterid
## 2623 2.1972246 0.4736842 1 0 4
## 2352 0.0000000 1.0000000 0 0 1
## 2100 0.0000000 1.0000000 0 0 1
## 2454 0.0000000 1.0000000 0 0 1
## 2632 0.6931472 0.1666667 0 0 2
## 2094 0.0000000 1.0000000 0 0 1
## .clusterid.fctr startprice.predict.All.X.glmnet
## 2623 4 147.8207
## 2352 1 464.9190
## 2100 1 456.8585
## 2454 1 432.6484
## 2632 2 278.9357
## 2094 1 415.4564
## startprice.predict.All.X.glmnet.err
## 2623 852.1693
## 2352 463.9290
## 2100 455.8585
## 2454 431.6584
## 2632 421.0643
## 2094 406.4564predct_accurate_var_name <- paste0(glb_rsp_var_out, glb_sel_mdl_id, ".accurate")
predct_error_var_name <- paste0(glb_rsp_var_out, glb_sel_mdl_id, ".err")
glb_OOBobs_df[, predct_accurate_var_name] <-
(glb_OOBobs_df[, glb_rsp_var] ==
glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)])
glb_featsimp_df <-
myget_feats_importance(mdl=glb_sel_mdl, featsimp_df=NULL)
glb_featsimp_df[, paste0(glb_sel_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## importance
## prdline.my.fctriPadAir 100.000000
## condition.fctrNew 89.605213
## D.npnct09.log 83.307569
## prdline.my.fctriPadmini 2+ 78.075306
## condition.fctrNew other (see details) 77.486529
## color.fctrGold 75.718418
## D.npnct10.log 69.483934
## carrier.fctrUnknown 68.783075
## D.terms.n.stem.stop.Ratio 68.180766
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 67.502712
## prdline.my.fctrUnknown:.clusterid.fctr2 67.410570
## D.TfIdf.sum.stem.stop.Ratio 67.012617
## prdline.my.fctriPadmini:.clusterid.fctr5 67.003081
## prdline.my.fctriPad 3+ 66.678300
## D.npnct03.log 65.767488
## condition.fctrManufacturer refurbished 65.196081
## cellular.fctr1 63.071826
## prdline.my.fctriPad 3+:.clusterid.fctr2 63.037048
## D.npnct01.log 62.769601
## prdline.my.fctriPad 1:.clusterid.fctr4 62.057198
## color.fctrWhite 60.273720
## color.fctrSpace Gray 60.150799
## prdline.my.fctriPad 3+:.clusterid.fctr4 58.938150
## prdline.my.fctriPadmini:.clusterid.fctr2 58.335982
## prdline.my.fctriPad 3+:.clusterid.fctr3 57.859425
## idseq.my 57.847819
## prdline.my.fctriPadmini 57.838522
## D.ratio.nstopwrds.nwrds 57.838522
## carrier.fctrAT&T 57.838522
## carrier.fctrOther 57.838522
## carrier.fctrT-Mobile 57.838522
## .rnorm 57.838522
## D.ndgts.log 57.838522
## D.npnct12.log 57.838522
## D.npnct16.log 57.838522
## D.npnct06.log 57.838522
## D.terms.n.post.stem 57.838522
## D.terms.n.post.stop 57.838522
## D.nstopwrds.log 57.838522
## D.nwrds.unq.log 57.838522
## D.terms.n.post.stem.log 57.838522
## D.terms.n.post.stop.log 57.838522
## D.nwrds.log 57.838522
## D.nchrs.log 57.838522
## D.nuppr.log 57.838522
## D.npnct24.log 57.838522
## D.TfIdf.sum.post.stem 57.838522
## D.sum.TfIdf 57.838522
## D.TfIdf.sum.post.stop 57.838522
## prdline.my.fctriPad 1:.clusterid.fctr3 57.838522
## prdline.my.fctriPad 2:.clusterid.fctr3 57.838522
## prdline.my.fctriPadmini:.clusterid.fctr3 57.838522
## prdline.my.fctrUnknown:.clusterid.fctr4 57.838522
## prdline.my.fctriPadAir:.clusterid.fctr4 57.838522
## prdline.my.fctriPadmini:.clusterid.fctr4 57.838522
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 57.838522
## prdline.my.fctrUnknown:.clusterid.fctr5 57.838522
## prdline.my.fctriPad 1:.clusterid.fctr5 57.838522
## prdline.my.fctriPad 2:.clusterid.fctr5 57.838522
## prdline.my.fctriPad 3+:.clusterid.fctr5 57.838522
## prdline.my.fctriPadAir:.clusterid.fctr5 57.838522
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 57.838522
## carrier.fctrVerizon 57.820657
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 57.472389
## prdline.my.fctriPadAir:.clusterid.fctr3 57.312195
## prdline.my.fctriPad 2:.clusterid.fctr2 56.864345
## D.npnct13.log 56.772512
## prdline.my.fctrUnknown:.clusterid.fctr3 55.718052
## D.npnct14.log 55.458341
## D.npnct08.log 55.292038
## color.fctrBlack 55.207252
## D.npnct11.log 55.159939
## D.npnct15.log 54.694738
## condition.fctrSeller refurbished 54.482458
## prdline.my.fctriPad 1:.clusterid.fctr2 54.212030
## D.npnct05.log 53.685990
## D.ratio.sum.TfIdf.nwrds 53.529001
## carrier.fctrSprint 51.737861
## prdline.my.fctriPad 2:.clusterid.fctr4 49.726710
## prdline.my.fctriPad 2 49.578069
## prdline.my.fctriPadAir:.clusterid.fctr2 46.524843
## D.npnct28.log 46.354146
## cellular.fctrUnknown 40.187677
## prdline.my.fctriPad 1 30.878770
## condition.fctrFor parts or not working 26.631947
## storage.fctr64 23.983863
## biddable 16.709485
## storage.fctr32 8.679021
## storage.fctrUnknown 4.194256
## storage.fctr16 0.000000
## All.X.glmnet.importance
## prdline.my.fctriPadAir 100.000000
## condition.fctrNew 89.605213
## D.npnct09.log 83.307569
## prdline.my.fctriPadmini 2+ 78.075306
## condition.fctrNew other (see details) 77.486529
## color.fctrGold 75.718418
## D.npnct10.log 69.483934
## carrier.fctrUnknown 68.783075
## D.terms.n.stem.stop.Ratio 68.180766
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 67.502712
## prdline.my.fctrUnknown:.clusterid.fctr2 67.410570
## D.TfIdf.sum.stem.stop.Ratio 67.012617
## prdline.my.fctriPadmini:.clusterid.fctr5 67.003081
## prdline.my.fctriPad 3+ 66.678300
## D.npnct03.log 65.767488
## condition.fctrManufacturer refurbished 65.196081
## cellular.fctr1 63.071826
## prdline.my.fctriPad 3+:.clusterid.fctr2 63.037048
## D.npnct01.log 62.769601
## prdline.my.fctriPad 1:.clusterid.fctr4 62.057198
## color.fctrWhite 60.273720
## color.fctrSpace Gray 60.150799
## prdline.my.fctriPad 3+:.clusterid.fctr4 58.938150
## prdline.my.fctriPadmini:.clusterid.fctr2 58.335982
## prdline.my.fctriPad 3+:.clusterid.fctr3 57.859425
## idseq.my 57.847819
## prdline.my.fctriPadmini 57.838522
## D.ratio.nstopwrds.nwrds 57.838522
## carrier.fctrAT&T 57.838522
## carrier.fctrOther 57.838522
## carrier.fctrT-Mobile 57.838522
## .rnorm 57.838522
## D.ndgts.log 57.838522
## D.npnct12.log 57.838522
## D.npnct16.log 57.838522
## D.npnct06.log 57.838522
## D.terms.n.post.stem 57.838522
## D.terms.n.post.stop 57.838522
## D.nstopwrds.log 57.838522
## D.nwrds.unq.log 57.838522
## D.terms.n.post.stem.log 57.838522
## D.terms.n.post.stop.log 57.838522
## D.nwrds.log 57.838522
## D.nchrs.log 57.838522
## D.nuppr.log 57.838522
## D.npnct24.log 57.838522
## D.TfIdf.sum.post.stem 57.838522
## D.sum.TfIdf 57.838522
## D.TfIdf.sum.post.stop 57.838522
## prdline.my.fctriPad 1:.clusterid.fctr3 57.838522
## prdline.my.fctriPad 2:.clusterid.fctr3 57.838522
## prdline.my.fctriPadmini:.clusterid.fctr3 57.838522
## prdline.my.fctrUnknown:.clusterid.fctr4 57.838522
## prdline.my.fctriPadAir:.clusterid.fctr4 57.838522
## prdline.my.fctriPadmini:.clusterid.fctr4 57.838522
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 57.838522
## prdline.my.fctrUnknown:.clusterid.fctr5 57.838522
## prdline.my.fctriPad 1:.clusterid.fctr5 57.838522
## prdline.my.fctriPad 2:.clusterid.fctr5 57.838522
## prdline.my.fctriPad 3+:.clusterid.fctr5 57.838522
## prdline.my.fctriPadAir:.clusterid.fctr5 57.838522
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 57.838522
## carrier.fctrVerizon 57.820657
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 57.472389
## prdline.my.fctriPadAir:.clusterid.fctr3 57.312195
## prdline.my.fctriPad 2:.clusterid.fctr2 56.864345
## D.npnct13.log 56.772512
## prdline.my.fctrUnknown:.clusterid.fctr3 55.718052
## D.npnct14.log 55.458341
## D.npnct08.log 55.292038
## color.fctrBlack 55.207252
## D.npnct11.log 55.159939
## D.npnct15.log 54.694738
## condition.fctrSeller refurbished 54.482458
## prdline.my.fctriPad 1:.clusterid.fctr2 54.212030
## D.npnct05.log 53.685990
## D.ratio.sum.TfIdf.nwrds 53.529001
## carrier.fctrSprint 51.737861
## prdline.my.fctriPad 2:.clusterid.fctr4 49.726710
## prdline.my.fctriPad 2 49.578069
## prdline.my.fctriPadAir:.clusterid.fctr2 46.524843
## D.npnct28.log 46.354146
## cellular.fctrUnknown 40.187677
## prdline.my.fctriPad 1 30.878770
## condition.fctrFor parts or not working 26.631947
## storage.fctr64 23.983863
## biddable 16.709485
## storage.fctr32 8.679021
## storage.fctrUnknown 4.194256
## storage.fctr16 0.000000# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
featsimp_df <- glb_featsimp_df
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <- ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -importance.max, summaryBy(importance ~ feat + feat.interact,
data=featsimp_df, FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ", nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- paste0(glb_rsp_var_out, mdl_id)
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars=var,
measure.vars=c(glb_rsp_var, rsp_var_out))
# if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value",
# facet_colcol_name="variable") +
# geom_vline(xintercept=<divider_val>, linetype="dotted")) else
print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
facet_colcol_name="variable", jitter=TRUE) +
guides(color=FALSE))
}
if (glb_is_regression) {
if (nrow(featsimp_df) == 0)
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glb_id_var)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (nrow(featsimp_df) == 0)
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var,
rsp_var_out=rsp_var_out,
id_vars=glb_id_var,
prob_threshold=prob_threshold)
# + geom_hline(yintercept=<divider_val>, linetype = "dotted")
)
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_OOBobs_df, mdl_id =
## glb_sel_mdl_id): Limiting important feature scatter plots to 5 out of 42
## UniqueID
## 2623 12625
## 2352 12354
## 2100 12102
## 2454 12456
## 2632 12634
## description
## 2623 Lot of 10 mixed iPad minis. Colors,models & storage capacity vary between each lot. There may be
## 2352
## 2100
## 2454
## 2632 Good condition IPAD 2 32gb wifi + 3g verizon. LOT OF FIVE.
## biddable startprice condition cellular carrier
## 2623 0 999.99 For parts or not working Unknown Unknown
## 2352 1 0.99 Used 0 None
## 2100 1 1.00 New 1 AT&T
## 2454 1 0.99 New 1 T-Mobile
## 2632 0 700.00 Used 1 Verizon
## color storage productline sold .src .grpid .rnorm idseq.my
## 2623 White Unknown Unknown NA Test <NA> -0.9259777 2625
## 2352 Gold 128 iPad Air 2 NA Test <NA> 0.1545570 2354
## 2100 Space Gray 128 iPad mini 2 NA Test <NA> 2.2673657 2102
## 2454 White 64 iPad Air 2 NA Test <NA> 0.4458716 2456
## 2632 Unknown 32 iPad 2 NA Test <NA> 0.8127608 2634
## prdline.my startprice.log
## 2623 iPadmini 6.90774528
## 2352 iPadAir -0.01005034
## 2100 iPadmini 2+ 0.00000000
## 2454 iPadAir -0.01005034
## 2632 iPad 2 6.55108034
## descr.my
## 2623 Lot of 10 mixed iPad minis. Colors, models & storage capacity vary between each lot. There may be
## 2352
## 2100
## 2454
## 2632 Good condition IPAD 2 32gb wifi + 3g verizon. LOT OF FIVE.
## condition.fctr cellular.fctr carrier.fctr color.fctr
## 2623 For parts or not working Unknown Unknown White
## 2352 Used 0 None Gold
## 2100 New 1 AT&T Space Gray
## 2454 New 1 T-Mobile White
## 2632 Used 1 Verizon Unknown
## storage.fctr prdline.my.fctr D.terms.n.post.stop
## 2623 Unknown iPadmini 8
## 2352 128 iPadAir 0
## 2100 128 iPadmini 2+ 0
## 2454 64 iPadAir 0
## 2632 32 iPad 2 8
## D.terms.n.post.stop.log D.TfIdf.sum.post.stop D.terms.n.post.stem
## 2623 2.197225 9.127623 8
## 2352 0.000000 0.000000 0
## 2100 0.000000 0.000000 0
## 2454 0.000000 0.000000 0
## 2632 2.197225 7.047501 8
## D.terms.n.post.stem.log D.TfIdf.sum.post.stem
## 2623 2.197225 8.069403
## 2352 0.000000 0.000000
## 2100 0.000000 0.000000
## 2454 0.000000 0.000000
## 2632 2.197225 6.969581
## D.terms.n.stem.stop.Ratio D.TfIdf.sum.stem.stop.Ratio D.T.condit
## 2623 1 0.8840640 0.0000000
## 2352 1 1.0000000 0.0000000
## 2100 1 1.0000000 0.0000000
## 2454 1 1.0000000 0.0000000
## 2632 1 0.9889437 0.3026733
## D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen D.T.great
## 2623 0 0 0 0.0000000 0.3908446 0 0
## 2352 0 0 0 0.0000000 0.0000000 0 0
## 2100 0 0 0 0.0000000 0.0000000 0 0
## 2454 0 0 0 0.0000000 0.0000000 0 0
## 2632 0 0 0 0.4691913 0.4397002 0 0
## D.T.work D.T.excel D.nwrds.log D.nwrds.unq.log D.sum.TfIdf
## 2623 0 0 2.944439 2.197225 8.069403
## 2352 0 0 0.000000 0.000000 0.000000
## 2100 0 0 0.000000 0.000000 0.000000
## 2454 0 0 0.000000 0.000000 0.000000
## 2632 0 0 2.484907 2.197225 6.969581
## D.ratio.sum.TfIdf.nwrds D.nchrs.log D.nuppr.log D.ndgts.log
## 2623 0.4483002 4.634729 4.356709 1.098612
## 2352 0.0000000 0.000000 0.000000 0.000000
## 2100 0.0000000 0.000000 0.000000 0.000000
## 2454 0.0000000 0.000000 0.000000 0.000000
## 2632 0.6335983 4.077537 3.713572 1.609438
## D.npnct01.log D.npnct03.log D.npnct05.log D.npnct06.log D.npnct08.log
## 2623 0 0 0 0.6931472 0
## 2352 0 0 0 0.0000000 0
## 2100 0 0 0 0.0000000 0
## 2454 0 0 0 0.0000000 0
## 2632 0 0 0 0.0000000 0
## D.npnct09.log D.npnct10.log D.npnct11.log D.npnct12.log D.npnct13.log
## 2623 0 0.0000000 0.6931472 0 1.098612
## 2352 0 0.0000000 0.0000000 0 0.000000
## 2100 0 0.0000000 0.0000000 0 0.000000
## 2454 0 0.0000000 0.0000000 0 0.000000
## 2632 0 0.6931472 0.0000000 0 1.098612
## D.npnct14.log D.npnct15.log D.npnct16.log D.npnct24.log D.npnct28.log
## 2623 0 0 0.6931472 0.6931472 0
## 2352 0 0 0.0000000 0.0000000 0
## 2100 0 0 0.0000000 0.0000000 0
## 2454 0 0 0.0000000 0.0000000 0
## 2632 0 0 0.0000000 0.6931472 0
## D.nstopwrds.log D.ratio.nstopwrds.nwrds D.P.mini D.P.air .clusterid
## 2623 2.1972246 0.4736842 1 0 4
## 2352 0.0000000 1.0000000 0 0 1
## 2100 0.0000000 1.0000000 0 0 1
## 2454 0.0000000 1.0000000 0 0 1
## 2632 0.6931472 0.1666667 0 0 2
## .clusterid.fctr startprice.predict.All.X.glmnet
## 2623 4 147.8207
## 2352 1 464.9190
## 2100 1 456.8585
## 2454 1 432.6484
## 2632 2 278.9357
## startprice.predict.All.X.glmnet.err
## 2623 852.1693
## 2352 463.9290
## 2100 455.8585
## 2454 431.6584
## 2632 421.0643
## startprice.predict.All.X.glmnet.accurate .label
## 2623 FALSE 12625
## 2352 FALSE 12354
## 2100 FALSE 12102
## 2454 FALSE 12456
## 2632 FALSE 12634
# gather predictions from models better than MFO.*
#mdl_id <- "Conditional.X.rf"
#mdl_id <- "Conditional.X.cp.0.rpart"
#mdl_id <- "Conditional.X.rpart"
# glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id,
# glb_rsp_var_out)
# print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, mdl_id)],
# glb_OOBobs_df[, glb_rsp_var])$table))
# FN_OOB_ids <- c(4721, 4020, 693, 92)
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_feats_df$id[1:5]])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
write.csv(glb_OOBobs_df[, c(glb_id_var,
grep(glb_rsp_var, names(glb_OOBobs_df), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# dsp_tbl(Headline.contains="[Ee]bola")
# sum(sel_obs(Headline.contains="[Ee]bola"))
# ftable(xtabs(Popular ~ NewsDesk.fctr, data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,]))
# xtabs(NewsDesk ~ Popular, #Popular ~ NewsDesk.fctr,
# data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# exclude=NULL)
# print(mycreate_xtab_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# tbl_col_names=c("Popular", "NewsDesk")))
# write.csv(glb_chunks_df, paste0(glb_out_pfx, tail(glb_chunks_df, 1)$label, "_",
# tail(glb_chunks_df, 1)$step_minor, "_chunks1.csv"),
# row.names=FALSE)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 12 fit.models 7 2 391.695 414.739 23.044
## 13 fit.models 7 3 414.740 NA NA if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
stop("fit.models_3: Why is this happening ?")## Warning in is.na(glb_allobs_df$D.P.http): is.na() applied to non-(list or
## vector) of type 'NULL'#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## [1] "startprice.predict.All.X.glmnet"
## [2] "startprice.predict.All.X.glmnet.err"
## [3] "startprice.predict.All.X.glmnet.accurate"for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
glb_model_type,
file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))
rm(ret_lst)## Warning in rm(ret_lst): object 'ret_lst' not foundreplay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 13 fit.models 7 3 414.740 420.743 6.003
## 14 fit.data.training 8 0 420.743 NA NAStep 8.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
stop("fit.data.training_0: Why is this happening ?")## Warning in is.na(glb_allobs_df$D.P.http): is.na() applied to non-(list or
## vector) of type 'NULL'# To create specific models
# glb_fin_mdl_id <- NULL; glb_fin_mdl <- NULL;
# glb_sel_mdl_id <- "Conditional.X.cp.0.rpart";
# glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]]; print(glb_sel_mdl)
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_sel_mdl
} else {
# print(mdl_feats_df <- myextract_mdl_feats(sel_mdl=glb_sel_mdl,
# entity_df=glb_fitobs_df))
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the model_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
tune_finmdl_df <- NULL
if (nrow(glb_sel_mdl$bestTune) > 0) {
for (param in names(glb_sel_mdl$bestTune)) {
#print(sprintf("param: %s", param))
if (glb_sel_mdl$bestTune[1, param] != "none")
tune_finmdl_df <- rbind(tune_finmdl_df,
data.frame(parameter=param,
min=glb_sel_mdl$bestTune[1, param],
max=glb_sel_mdl$bestTune[1, param],
by=1)) # by val does not matter
}
}
# Sync with parameters in mydsutils.R
require(gdata)
ret_lst <- myfit_mdl(model_id="Final", model_method=model_method,
indep_vars_vctr=trim(unlist(strsplit(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"feats"], "[,]"))),
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnobs_df, OOB_df=NULL,
n_cv_folds=glb_n_cv_folds, tune_models_df=tune_finmdl_df,
# Automate from here
# Issues if glb_sel_mdl$method == "rf" b/c trainControl is "oob"; not "cv"
model_loss_mtrx=glb_model_metric_terms,
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize)
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "model_id"]
}## [1] "fitting model: Final.glmnet"
## [1] " indep_vars: prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
## Length Class Mode
## a0 94 -none- numeric
## beta 8460 dgCMatrix S4
## df 94 -none- numeric
## dim 2 -none- numeric
## lambda 94 -none- numeric
## dev.ratio 94 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 90 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 294.78257637
## prdline.my.fctriPad 1
## -76.71045424
## prdline.my.fctriPad 2
## -23.64322090
## prdline.my.fctriPad 3+
## 24.95136901
## prdline.my.fctriPadAir
## 119.79418098
## prdline.my.fctriPadmini 2+
## 57.44456493
## condition.fctrFor parts or not working
## -88.58209077
## condition.fctrManufacturer refurbished
## 20.69027409
## condition.fctrNew
## 90.44265248
## condition.fctrNew other (see details)
## 55.84012240
## condition.fctrSeller refurbished
## -9.35222586
## color.fctrBlack
## -7.46170465
## color.fctrGold
## 50.76406013
## color.fctrSpace Gray
## 6.39766654
## color.fctrWhite
## 6.80464281
## D.TfIdf.sum.stem.stop.Ratio
## 25.43746154
## idseq.my
## 0.02633208
## carrier.fctrSprint
## -16.90344697
## carrier.fctrUnknown
## 30.88068200
## D.npnct09.log
## 71.33981931
## D.npnct10.log
## 32.00751631
## D.terms.n.stem.stop.Ratio
## 27.42161833
## D.npnct28.log
## -31.23070785
## cellular.fctr1
## 14.88145023
## cellular.fctrUnknown
## -49.84674880
## D.npnct14.log
## -6.44241930
## D.npnct05.log
## -11.72906659
## D.npnct08.log
## -6.83561783
## D.npnct01.log
## 13.77599628
## D.npnct15.log
## -8.60514295
## D.npnct11.log
## -7.59188211
## D.npnct03.log
## 21.92795604
## storage.fctr16
## -162.98155887
## storage.fctr32
## -138.23288130
## storage.fctr64
## -94.74980540
## storage.fctrUnknown
## -151.03929961
## D.npnct13.log
## -2.99822888
## D.ratio.sum.TfIdf.nwrds
## -12.14704821
## biddable
## -116.86488285
## prdline.my.fctrUnknown:.clusterid.fctr2
## 26.62965681
## prdline.my.fctriPad 1:.clusterid.fctr2
## -10.22155294
## prdline.my.fctriPad 2:.clusterid.fctr2
## -2.72772819
## prdline.my.fctriPad 3+:.clusterid.fctr2
## 14.53079907
## prdline.my.fctriPadAir:.clusterid.fctr2
## -31.90398968
## prdline.my.fctriPadmini:.clusterid.fctr2
## 0.88956465
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 27.16155447
## prdline.my.fctrUnknown:.clusterid.fctr3
## -5.97461085
## prdline.my.fctriPadAir:.clusterid.fctr3
## -1.17994114
## prdline.my.fctriPadmini 2+:.clusterid.fctr3
## -0.66788627
## prdline.my.fctriPad 1:.clusterid.fctr4
## 11.38256051
## prdline.my.fctriPad 2:.clusterid.fctr4
## -22.79555224
## prdline.my.fctriPad 3+:.clusterid.fctr4
## 2.82723340
## prdline.my.fctriPadmini:.clusterid.fctr5
## 25.38093863
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 1.998775e+02
## prdline.my.fctriPad 1
## -7.644313e+01
## prdline.my.fctriPad 2
## -1.999249e+01
## prdline.my.fctriPad 3+
## 2.914958e+01
## prdline.my.fctriPadAir
## 1.234229e+02
## prdline.my.fctriPadmini
## 3.465888e+00
## prdline.my.fctriPadmini 2+
## 6.086067e+01
## condition.fctrFor parts or not working
## -9.227346e+01
## condition.fctrManufacturer refurbished
## 2.554944e+01
## condition.fctrNew
## 8.687130e+01
## condition.fctrNew other (see details)
## 5.599857e+01
## condition.fctrSeller refurbished
## -1.402468e+01
## color.fctrBlack
## -7.412825e+00
## color.fctrGold
## 5.278243e+01
## color.fctrSpace Gray
## 1.063730e+01
## color.fctrWhite
## 9.549012e+00
## D.TfIdf.sum.stem.stop.Ratio
## 6.591341e+01
## D.ratio.nstopwrds.nwrds
## 2.869129e+01
## idseq.my
## 2.824860e-02
## carrier.fctrOther
## -9.326523e+00
## carrier.fctrSprint
## -3.086106e+01
## carrier.fctrT-Mobile
## -4.973113e+00
## carrier.fctrUnknown
## 3.307679e+01
## carrier.fctrVerizon
## -4.757925e+00
## D.npnct09.log
## 9.151029e+01
## D.npnct10.log
## 5.383163e+01
## D.terms.n.stem.stop.Ratio
## 8.575788e+01
## D.npnct28.log
## -6.770364e+01
## cellular.fctr1
## 1.701378e+01
## cellular.fctrUnknown
## -5.221195e+01
## D.npnct14.log
## -1.732644e+01
## .rnorm
## -2.606380e-01
## D.npnct05.log
## -1.292165e+01
## D.npnct08.log
## -1.823787e+01
## D.npnct01.log
## 1.689144e+01
## D.ndgts.log
## -1.668628e+00
## D.npnct12.log
## 2.053090e+00
## D.npnct16.log
## 1.070520e+01
## D.npnct06.log
## -1.535096e+01
## D.npnct15.log
## -2.019071e+01
## D.npnct11.log
## -1.121243e+01
## D.npnct03.log
## 3.598661e+01
## storage.fctr16
## -2.008292e+02
## storage.fctr32
## -1.783456e+02
## storage.fctr64
## -1.335379e+02
## storage.fctrUnknown
## -1.901398e+02
## D.npnct13.log
## -7.450029e+00
## D.terms.n.post.stop
## -1.798417e+00
## D.ratio.sum.TfIdf.nwrds
## -1.186745e+01
## D.nstopwrds.log
## -2.528831e+01
## D.nwrds.unq.log
## -2.345913e+00
## D.terms.n.post.stem.log
## -1.005925e-02
## D.terms.n.post.stop.log
## -1.337724e-03
## D.nwrds.log
## 4.736143e+01
## D.nchrs.log
## 3.200722e+00
## D.nuppr.log
## -7.287495e+00
## D.npnct24.log
## -5.018553e+01
## D.TfIdf.sum.post.stop
## 1.644332e+00
## biddable
## -1.180910e+02
## prdline.my.fctrUnknown:.clusterid.fctr2
## 4.104686e+01
## prdline.my.fctriPad 1:.clusterid.fctr2
## -8.957112e+00
## prdline.my.fctriPad 2:.clusterid.fctr2
## -1.877347e+00
## prdline.my.fctriPad 3+:.clusterid.fctr2
## 2.133655e+01
## prdline.my.fctriPadAir:.clusterid.fctr2
## -3.824864e+01
## prdline.my.fctriPadmini:.clusterid.fctr2
## 1.142155e+01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 3.398284e+01
## prdline.my.fctrUnknown:.clusterid.fctr3
## -4.613406e+00
## prdline.my.fctriPad 1:.clusterid.fctr3
## 1.277467e+01
## prdline.my.fctriPad 2:.clusterid.fctr3
## 1.452679e+01
## prdline.my.fctriPad 3+:.clusterid.fctr3
## 8.868811e+00
## prdline.my.fctriPadAir:.clusterid.fctr3
## -1.113572e+01
## prdline.my.fctriPadmini:.clusterid.fctr3
## -2.720336e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr3
## -7.412104e+00
## prdline.my.fctriPad 1:.clusterid.fctr4
## 2.954268e+01
## prdline.my.fctriPad 2:.clusterid.fctr4
## -2.510686e+01
## prdline.my.fctriPad 3+:.clusterid.fctr4
## 1.131957e+01
## prdline.my.fctriPadAir:.clusterid.fctr4
## -1.209120e+01
## prdline.my.fctriPadmini:.clusterid.fctr4
## -5.445225e+00
## prdline.my.fctriPad 2:.clusterid.fctr5
## 1.254241e+01
## prdline.my.fctriPadmini:.clusterid.fctr5
## 4.135022e+01
## character(0)
## character(0)
## [1] " calling mypredict_mdl for fit:"
## model_id model_method
## 1 Final.glmnet glmnet
## feats
## 1 prdline.my.fctr, condition.fctr, color.fctr, D.TfIdf.sum.stem.stop.Ratio, D.ratio.nstopwrds.nwrds, idseq.my, carrier.fctr, D.npnct09.log, D.npnct10.log, D.terms.n.stem.stop.Ratio, D.npnct28.log, cellular.fctr, D.npnct14.log, .rnorm, D.npnct05.log, D.npnct08.log, D.npnct01.log, D.ndgts.log, D.npnct12.log, D.npnct16.log, D.npnct06.log, D.npnct15.log, D.npnct11.log, D.npnct03.log, storage.fctr, D.npnct13.log, D.terms.n.post.stem, D.terms.n.post.stop, D.ratio.sum.TfIdf.nwrds, D.nstopwrds.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.nwrds.log, D.nchrs.log, D.nuppr.log, D.npnct24.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, D.TfIdf.sum.post.stop, biddable, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.24 0.042
## max.R.sq.fit min.RMSE.fit max.Rsquared.fit min.RMSESD.fit
## 1 0.6647981 101.8988 0.6414783 5.733788
## max.RsquaredSD.fit
## 1 0.04264401rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 14 fit.data.training 8 0 420.743 423.553 2.811
## 15 fit.data.training 8 1 423.554 NA NA#```
#```{r fit.data.training_1, cache=FALSE}
glb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "opt.prob.threshold.OOB"], NULL))## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
## UniqueID description biddable startprice
## 1396 11397 0 999.00
## 1418 11419 1 700.00
## 1764 11765 1 500.00
## 1234 11235 1 0.99
## 1019 11019 0 720.12
## 1115 11115 New, sealed, box not included 1 0.99
## condition cellular carrier color storage productline
## 1396 Used 0 None Unknown 32 iPad mini
## 1418 Used Unknown Unknown Unknown Unknown Unknown
## 1764 For parts or not working Unknown Unknown Unknown Unknown Unknown
## 1234 New other (see details) 1 Unknown Gold 64 iPad Air 2
## 1019 Used 1 AT&T Unknown 64 iPad mini
## 1115 New other (see details) 0 None Unknown 128 iPad Air 2
## sold .src .grpid .rnorm idseq.my prdline.my startprice.log
## 1396 0 Train <NA> -0.1429904 1397 iPadmini 6.90675478
## 1418 0 Train <NA> 0.7258252 1419 Unknown 6.55108034
## 1764 0 Train <NA> 0.1484839 1765 Unknown 6.21460810
## 1234 1 Train <NA> -1.5901469 1235 iPadAir -0.01005034
## 1019 0 Train <NA> 0.7141838 1019 iPadmini 6.57941786
## 1115 1 Train <NA> 1.5179876 1115 iPadAir -0.01005034
## descr.my condition.fctr cellular.fctr
## 1396 Used 0
## 1418 Used Unknown
## 1764 For parts or not working Unknown
## 1234 New other (see details) 1
## 1019 Used 1
## 1115 New, sealed, box not included New other (see details) 0
## carrier.fctr color.fctr storage.fctr prdline.my.fctr
## 1396 None Unknown 32 iPadmini
## 1418 Unknown Unknown Unknown Unknown
## 1764 Unknown Unknown Unknown Unknown
## 1234 Unknown Gold 64 iPadAir
## 1019 AT&T Unknown 64 iPadmini
## 1115 None Unknown 128 iPadAir
## D.terms.n.post.stop D.terms.n.post.stop.log D.TfIdf.sum.post.stop
## 1396 0 0.000000 0.000000
## 1418 0 0.000000 0.000000
## 1764 0 0.000000 0.000000
## 1234 0 0.000000 0.000000
## 1019 0 0.000000 0.000000
## 1115 4 1.609438 5.867446
## D.terms.n.post.stem D.terms.n.post.stem.log D.TfIdf.sum.post.stem
## 1396 0 0.000000 0.00000
## 1418 0 0.000000 0.00000
## 1764 0 0.000000 0.00000
## 1234 0 0.000000 0.00000
## 1019 0 0.000000 0.00000
## 1115 4 1.609438 5.43465
## D.terms.n.stem.stop.Ratio D.TfIdf.sum.stem.stop.Ratio D.T.condit
## 1396 1 1.0000000 0
## 1418 1 1.0000000 0
## 1764 1 1.0000000 0
## 1234 1 1.0000000 0
## 1019 1 1.0000000 0
## 1115 1 0.9262378 0
## D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen D.T.great
## 1396 0 0 0.000000 0 0 0 0
## 1418 0 0 0.000000 0 0 0 0
## 1764 0 0 0.000000 0 0 0 0
## 1234 0 0 0.000000 0 0 0 0
## 1019 0 0 0.000000 0 0 0 0
## 1115 0 0 1.022545 0 0 0 0
## D.T.work D.T.excel D.nwrds.log D.nwrds.unq.log D.sum.TfIdf
## 1396 0 0 0.000000 0.000000 0.00000
## 1418 0 0 0.000000 0.000000 0.00000
## 1764 0 0 0.000000 0.000000 0.00000
## 1234 0 0 0.000000 0.000000 0.00000
## 1019 0 0 0.000000 0.000000 0.00000
## 1115 0 0 1.791759 1.609438 5.43465
## D.ratio.sum.TfIdf.nwrds D.nchrs.log D.nuppr.log D.ndgts.log
## 1396 0.00000 0.000000 0.000000 0
## 1418 0.00000 0.000000 0.000000 0
## 1764 0.00000 0.000000 0.000000 0
## 1234 0.00000 0.000000 0.000000 0
## 1019 0.00000 0.000000 0.000000 0
## 1115 1.08693 3.401197 3.178054 0
## D.npnct01.log D.npnct03.log D.npnct05.log D.npnct06.log D.npnct08.log
## 1396 0 0 0 0 0
## 1418 0 0 0 0 0
## 1764 0 0 0 0 0
## 1234 0 0 0 0 0
## 1019 0 0 0 0 0
## 1115 0 0 0 0 0
## D.npnct09.log D.npnct10.log D.npnct11.log D.npnct12.log D.npnct13.log
## 1396 0 0 0.000000 0 0
## 1418 0 0 0.000000 0 0
## 1764 0 0 0.000000 0 0
## 1234 0 0 0.000000 0 0
## 1019 0 0 0.000000 0 0
## 1115 0 0 1.098612 0 0
## D.npnct14.log D.npnct15.log D.npnct16.log D.npnct24.log D.npnct28.log
## 1396 0 0 0 0.0000000 0
## 1418 0 0 0 0.0000000 0
## 1764 0 0 0 0.0000000 0
## 1234 0 0 0 0.0000000 0
## 1019 0 0 0 0.0000000 0
## 1115 0 0 0 0.6931472 0
## D.nstopwrds.log D.ratio.nstopwrds.nwrds D.P.mini D.P.air .clusterid
## 1396 0.0000000 1.0000000 0 0 1
## 1418 0.0000000 1.0000000 0 0 1
## 1764 0.0000000 1.0000000 0 0 1
## 1234 0.0000000 1.0000000 0 0 1
## 1019 0.0000000 1.0000000 0 0 1
## 1115 0.6931472 0.3333333 0 0 4
## .clusterid.fctr startprice.predict.Final.glmnet
## 1396 1 246.09809
## 1418 1 97.93565
## 1764 1 18.36205
## 1234 1 440.94570
## 1019 1 294.49596
## 1115 4 413.58975
## startprice.predict.Final.glmnet.err
## 1396 752.9019
## 1418 602.0643
## 1764 481.6380
## 1234 439.9557
## 1019 425.6240
## 1115 412.5998sav_featsimp_df <- glb_featsimp_df
#glb_feats_df <- sav_feats_df
# glb_feats_df <- mymerge_feats_importance(feats_df=glb_feats_df, sel_mdl=glb_fin_mdl,
# entity_df=glb_trnobs_df)
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl, featsimp_df=glb_featsimp_df)
glb_featsimp_df[, paste0(glb_fin_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## All.X.glmnet.importance
## prdline.my.fctriPadAir 100.000000
## condition.fctrNew 89.605213
## D.npnct09.log 83.307569
## prdline.my.fctriPadmini 2+ 78.075306
## condition.fctrNew other (see details) 77.486529
## color.fctrGold 75.718418
## D.npnct10.log 69.483934
## carrier.fctrUnknown 68.783075
## D.terms.n.stem.stop.Ratio 68.180766
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 67.502712
## prdline.my.fctrUnknown:.clusterid.fctr2 67.410570
## D.TfIdf.sum.stem.stop.Ratio 67.012617
## prdline.my.fctriPadmini:.clusterid.fctr5 67.003081
## prdline.my.fctriPad 3+ 66.678300
## D.npnct03.log 65.767488
## condition.fctrManufacturer refurbished 65.196081
## cellular.fctr1 63.071826
## prdline.my.fctriPad 3+:.clusterid.fctr2 63.037048
## D.npnct01.log 62.769601
## prdline.my.fctriPad 1:.clusterid.fctr4 62.057198
## color.fctrWhite 60.273720
## color.fctrSpace Gray 60.150799
## prdline.my.fctriPad 3+:.clusterid.fctr4 58.938150
## prdline.my.fctriPadmini:.clusterid.fctr2 58.335982
## prdline.my.fctriPad 3+:.clusterid.fctr3 57.859425
## idseq.my 57.847819
## .rnorm 57.838522
## D.TfIdf.sum.post.stem 57.838522
## D.TfIdf.sum.post.stop 57.838522
## D.nchrs.log 57.838522
## D.ndgts.log 57.838522
## D.npnct06.log 57.838522
## D.npnct12.log 57.838522
## D.npnct16.log 57.838522
## D.npnct24.log 57.838522
## D.nstopwrds.log 57.838522
## D.nuppr.log 57.838522
## D.nwrds.log 57.838522
## D.nwrds.unq.log 57.838522
## D.ratio.nstopwrds.nwrds 57.838522
## D.sum.TfIdf 57.838522
## D.terms.n.post.stem 57.838522
## D.terms.n.post.stem.log 57.838522
## D.terms.n.post.stop 57.838522
## D.terms.n.post.stop.log 57.838522
## carrier.fctrAT&T 57.838522
## carrier.fctrOther 57.838522
## carrier.fctrT-Mobile 57.838522
## prdline.my.fctrUnknown:.clusterid.fctr4 57.838522
## prdline.my.fctrUnknown:.clusterid.fctr5 57.838522
## prdline.my.fctriPad 1:.clusterid.fctr3 57.838522
## prdline.my.fctriPad 1:.clusterid.fctr5 57.838522
## prdline.my.fctriPad 2:.clusterid.fctr3 57.838522
## prdline.my.fctriPad 2:.clusterid.fctr5 57.838522
## prdline.my.fctriPad 3+:.clusterid.fctr5 57.838522
## prdline.my.fctriPadAir:.clusterid.fctr4 57.838522
## prdline.my.fctriPadAir:.clusterid.fctr5 57.838522
## prdline.my.fctriPadmini 57.838522
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 57.838522
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 57.838522
## prdline.my.fctriPadmini:.clusterid.fctr3 57.838522
## prdline.my.fctriPadmini:.clusterid.fctr4 57.838522
## carrier.fctrVerizon 57.820657
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 57.472389
## prdline.my.fctriPadAir:.clusterid.fctr3 57.312195
## prdline.my.fctriPad 2:.clusterid.fctr2 56.864345
## D.npnct13.log 56.772512
## prdline.my.fctrUnknown:.clusterid.fctr3 55.718052
## D.npnct14.log 55.458341
## D.npnct08.log 55.292038
## color.fctrBlack 55.207252
## D.npnct11.log 55.159939
## D.npnct15.log 54.694738
## condition.fctrSeller refurbished 54.482458
## prdline.my.fctriPad 1:.clusterid.fctr2 54.212030
## D.npnct05.log 53.685990
## D.ratio.sum.TfIdf.nwrds 53.529001
## carrier.fctrSprint 51.737861
## prdline.my.fctriPad 2:.clusterid.fctr4 49.726710
## prdline.my.fctriPad 2 49.578069
## prdline.my.fctriPadAir:.clusterid.fctr2 46.524843
## D.npnct28.log 46.354146
## cellular.fctrUnknown 40.187677
## prdline.my.fctriPad 1 30.878770
## condition.fctrFor parts or not working 26.631947
## storage.fctr64 23.983863
## biddable 16.709485
## storage.fctr32 8.679021
## storage.fctrUnknown 4.194256
## storage.fctr16 0.000000
## importance
## prdline.my.fctriPadAir 100.000000
## condition.fctrNew 89.605213
## D.npnct09.log 83.307569
## prdline.my.fctriPadmini 2+ 78.075306
## condition.fctrNew other (see details) 77.486529
## color.fctrGold 75.718418
## D.npnct10.log 69.483934
## carrier.fctrUnknown 68.783075
## D.terms.n.stem.stop.Ratio 68.180766
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 67.502712
## prdline.my.fctrUnknown:.clusterid.fctr2 67.410570
## D.TfIdf.sum.stem.stop.Ratio 67.012617
## prdline.my.fctriPadmini:.clusterid.fctr5 67.003081
## prdline.my.fctriPad 3+ 66.678300
## D.npnct03.log 65.767488
## condition.fctrManufacturer refurbished 65.196081
## cellular.fctr1 63.071826
## prdline.my.fctriPad 3+:.clusterid.fctr2 63.037048
## D.npnct01.log 62.769601
## prdline.my.fctriPad 1:.clusterid.fctr4 62.057198
## color.fctrWhite 60.273720
## color.fctrSpace Gray 60.150799
## prdline.my.fctriPad 3+:.clusterid.fctr4 58.938150
## prdline.my.fctriPadmini:.clusterid.fctr2 58.335982
## prdline.my.fctriPad 3+:.clusterid.fctr3 57.859425
## idseq.my 57.847819
## .rnorm 57.838522
## D.TfIdf.sum.post.stem 57.838522
## D.TfIdf.sum.post.stop 57.838522
## D.nchrs.log 57.838522
## D.ndgts.log 57.838522
## D.npnct06.log 57.838522
## D.npnct12.log 57.838522
## D.npnct16.log 57.838522
## D.npnct24.log 57.838522
## D.nstopwrds.log 57.838522
## D.nuppr.log 57.838522
## D.nwrds.log 57.838522
## D.nwrds.unq.log 57.838522
## D.ratio.nstopwrds.nwrds 57.838522
## D.sum.TfIdf 57.838522
## D.terms.n.post.stem 57.838522
## D.terms.n.post.stem.log 57.838522
## D.terms.n.post.stop 57.838522
## D.terms.n.post.stop.log 57.838522
## carrier.fctrAT&T 57.838522
## carrier.fctrOther 57.838522
## carrier.fctrT-Mobile 57.838522
## prdline.my.fctrUnknown:.clusterid.fctr4 57.838522
## prdline.my.fctrUnknown:.clusterid.fctr5 57.838522
## prdline.my.fctriPad 1:.clusterid.fctr3 57.838522
## prdline.my.fctriPad 1:.clusterid.fctr5 57.838522
## prdline.my.fctriPad 2:.clusterid.fctr3 57.838522
## prdline.my.fctriPad 2:.clusterid.fctr5 57.838522
## prdline.my.fctriPad 3+:.clusterid.fctr5 57.838522
## prdline.my.fctriPadAir:.clusterid.fctr4 57.838522
## prdline.my.fctriPadAir:.clusterid.fctr5 57.838522
## prdline.my.fctriPadmini 57.838522
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 57.838522
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 57.838522
## prdline.my.fctriPadmini:.clusterid.fctr3 57.838522
## prdline.my.fctriPadmini:.clusterid.fctr4 57.838522
## carrier.fctrVerizon 57.820657
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 57.472389
## prdline.my.fctriPadAir:.clusterid.fctr3 57.312195
## prdline.my.fctriPad 2:.clusterid.fctr2 56.864345
## D.npnct13.log 56.772512
## prdline.my.fctrUnknown:.clusterid.fctr3 55.718052
## D.npnct14.log 55.458341
## D.npnct08.log 55.292038
## color.fctrBlack 55.207252
## D.npnct11.log 55.159939
## D.npnct15.log 54.694738
## condition.fctrSeller refurbished 54.482458
## prdline.my.fctriPad 1:.clusterid.fctr2 54.212030
## D.npnct05.log 53.685990
## D.ratio.sum.TfIdf.nwrds 53.529001
## carrier.fctrSprint 51.737861
## prdline.my.fctriPad 2:.clusterid.fctr4 49.726710
## prdline.my.fctriPad 2 49.578069
## prdline.my.fctriPadAir:.clusterid.fctr2 46.524843
## D.npnct28.log 46.354146
## cellular.fctrUnknown 40.187677
## prdline.my.fctriPad 1 30.878770
## condition.fctrFor parts or not working 26.631947
## storage.fctr64 23.983863
## biddable 16.709485
## storage.fctr32 8.679021
## storage.fctrUnknown 4.194256
## storage.fctr16 0.000000
## Final.glmnet.importance
## prdline.my.fctriPadAir 100.000000
## condition.fctrNew 89.605213
## D.npnct09.log 83.307569
## prdline.my.fctriPadmini 2+ 78.075306
## condition.fctrNew other (see details) 77.486529
## color.fctrGold 75.718418
## D.npnct10.log 69.483934
## carrier.fctrUnknown 68.783075
## D.terms.n.stem.stop.Ratio 68.180766
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 67.502712
## prdline.my.fctrUnknown:.clusterid.fctr2 67.410570
## D.TfIdf.sum.stem.stop.Ratio 67.012617
## prdline.my.fctriPadmini:.clusterid.fctr5 67.003081
## prdline.my.fctriPad 3+ 66.678300
## D.npnct03.log 65.767488
## condition.fctrManufacturer refurbished 65.196081
## cellular.fctr1 63.071826
## prdline.my.fctriPad 3+:.clusterid.fctr2 63.037048
## D.npnct01.log 62.769601
## prdline.my.fctriPad 1:.clusterid.fctr4 62.057198
## color.fctrWhite 60.273720
## color.fctrSpace Gray 60.150799
## prdline.my.fctriPad 3+:.clusterid.fctr4 58.938150
## prdline.my.fctriPadmini:.clusterid.fctr2 58.335982
## prdline.my.fctriPad 3+:.clusterid.fctr3 57.859425
## idseq.my 57.847819
## .rnorm 57.838522
## D.TfIdf.sum.post.stem 57.838522
## D.TfIdf.sum.post.stop 57.838522
## D.nchrs.log 57.838522
## D.ndgts.log 57.838522
## D.npnct06.log 57.838522
## D.npnct12.log 57.838522
## D.npnct16.log 57.838522
## D.npnct24.log 57.838522
## D.nstopwrds.log 57.838522
## D.nuppr.log 57.838522
## D.nwrds.log 57.838522
## D.nwrds.unq.log 57.838522
## D.ratio.nstopwrds.nwrds 57.838522
## D.sum.TfIdf 57.838522
## D.terms.n.post.stem 57.838522
## D.terms.n.post.stem.log 57.838522
## D.terms.n.post.stop 57.838522
## D.terms.n.post.stop.log 57.838522
## carrier.fctrAT&T 57.838522
## carrier.fctrOther 57.838522
## carrier.fctrT-Mobile 57.838522
## prdline.my.fctrUnknown:.clusterid.fctr4 57.838522
## prdline.my.fctrUnknown:.clusterid.fctr5 57.838522
## prdline.my.fctriPad 1:.clusterid.fctr3 57.838522
## prdline.my.fctriPad 1:.clusterid.fctr5 57.838522
## prdline.my.fctriPad 2:.clusterid.fctr3 57.838522
## prdline.my.fctriPad 2:.clusterid.fctr5 57.838522
## prdline.my.fctriPad 3+:.clusterid.fctr5 57.838522
## prdline.my.fctriPadAir:.clusterid.fctr4 57.838522
## prdline.my.fctriPadAir:.clusterid.fctr5 57.838522
## prdline.my.fctriPadmini 57.838522
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 57.838522
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 57.838522
## prdline.my.fctriPadmini:.clusterid.fctr3 57.838522
## prdline.my.fctriPadmini:.clusterid.fctr4 57.838522
## carrier.fctrVerizon 57.820657
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 57.472389
## prdline.my.fctriPadAir:.clusterid.fctr3 57.312195
## prdline.my.fctriPad 2:.clusterid.fctr2 56.864345
## D.npnct13.log 56.772512
## prdline.my.fctrUnknown:.clusterid.fctr3 55.718052
## D.npnct14.log 55.458341
## D.npnct08.log 55.292038
## color.fctrBlack 55.207252
## D.npnct11.log 55.159939
## D.npnct15.log 54.694738
## condition.fctrSeller refurbished 54.482458
## prdline.my.fctriPad 1:.clusterid.fctr2 54.212030
## D.npnct05.log 53.685990
## D.ratio.sum.TfIdf.nwrds 53.529001
## carrier.fctrSprint 51.737861
## prdline.my.fctriPad 2:.clusterid.fctr4 49.726710
## prdline.my.fctriPad 2 49.578069
## prdline.my.fctriPadAir:.clusterid.fctr2 46.524843
## D.npnct28.log 46.354146
## cellular.fctrUnknown 40.187677
## prdline.my.fctriPad 1 30.878770
## condition.fctrFor parts or not working 26.631947
## storage.fctr64 23.983863
## biddable 16.709485
## storage.fctr32 8.679021
## storage.fctrUnknown 4.194256
## storage.fctr16 0.000000if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id): Limiting important feature scatter plots to 5 out of 42
## UniqueID description biddable startprice condition
## 1396 11397 0 999.00 Used
## 1418 11419 1 700.00 Used
## 1764 11765 1 500.00 For parts or not working
## 1234 11235 1 0.99 New other (see details)
## 1019 11019 0 720.12 Used
## cellular carrier color storage productline sold .src .grpid
## 1396 0 None Unknown 32 iPad mini 0 Train <NA>
## 1418 Unknown Unknown Unknown Unknown Unknown 0 Train <NA>
## 1764 Unknown Unknown Unknown Unknown Unknown 0 Train <NA>
## 1234 1 Unknown Gold 64 iPad Air 2 1 Train <NA>
## 1019 1 AT&T Unknown 64 iPad mini 0 Train <NA>
## .rnorm idseq.my prdline.my startprice.log descr.my
## 1396 -0.1429904 1397 iPadmini 6.90675478
## 1418 0.7258252 1419 Unknown 6.55108034
## 1764 0.1484839 1765 Unknown 6.21460810
## 1234 -1.5901469 1235 iPadAir -0.01005034
## 1019 0.7141838 1019 iPadmini 6.57941786
## condition.fctr cellular.fctr carrier.fctr color.fctr
## 1396 Used 0 None Unknown
## 1418 Used Unknown Unknown Unknown
## 1764 For parts or not working Unknown Unknown Unknown
## 1234 New other (see details) 1 Unknown Gold
## 1019 Used 1 AT&T Unknown
## storage.fctr prdline.my.fctr D.terms.n.post.stop
## 1396 32 iPadmini 0
## 1418 Unknown Unknown 0
## 1764 Unknown Unknown 0
## 1234 64 iPadAir 0
## 1019 64 iPadmini 0
## D.terms.n.post.stop.log D.TfIdf.sum.post.stop D.terms.n.post.stem
## 1396 0 0 0
## 1418 0 0 0
## 1764 0 0 0
## 1234 0 0 0
## 1019 0 0 0
## D.terms.n.post.stem.log D.TfIdf.sum.post.stem
## 1396 0 0
## 1418 0 0
## 1764 0 0
## 1234 0 0
## 1019 0 0
## D.terms.n.stem.stop.Ratio D.TfIdf.sum.stem.stop.Ratio D.T.condit
## 1396 1 1 0
## 1418 1 1 0
## 1764 1 1 0
## 1234 1 1 0
## 1019 1 1 0
## D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen D.T.great
## 1396 0 0 0 0 0 0 0
## 1418 0 0 0 0 0 0 0
## 1764 0 0 0 0 0 0 0
## 1234 0 0 0 0 0 0 0
## 1019 0 0 0 0 0 0 0
## D.T.work D.T.excel D.nwrds.log D.nwrds.unq.log D.sum.TfIdf
## 1396 0 0 0 0 0
## 1418 0 0 0 0 0
## 1764 0 0 0 0 0
## 1234 0 0 0 0 0
## 1019 0 0 0 0 0
## D.ratio.sum.TfIdf.nwrds D.nchrs.log D.nuppr.log D.ndgts.log
## 1396 0 0 0 0
## 1418 0 0 0 0
## 1764 0 0 0 0
## 1234 0 0 0 0
## 1019 0 0 0 0
## D.npnct01.log D.npnct03.log D.npnct05.log D.npnct06.log D.npnct08.log
## 1396 0 0 0 0 0
## 1418 0 0 0 0 0
## 1764 0 0 0 0 0
## 1234 0 0 0 0 0
## 1019 0 0 0 0 0
## D.npnct09.log D.npnct10.log D.npnct11.log D.npnct12.log D.npnct13.log
## 1396 0 0 0 0 0
## 1418 0 0 0 0 0
## 1764 0 0 0 0 0
## 1234 0 0 0 0 0
## 1019 0 0 0 0 0
## D.npnct14.log D.npnct15.log D.npnct16.log D.npnct24.log D.npnct28.log
## 1396 0 0 0 0 0
## 1418 0 0 0 0 0
## 1764 0 0 0 0 0
## 1234 0 0 0 0 0
## 1019 0 0 0 0 0
## D.nstopwrds.log D.ratio.nstopwrds.nwrds D.P.mini D.P.air .clusterid
## 1396 0 1 0 0 1
## 1418 0 1 0 0 1
## 1764 0 1 0 0 1
## 1234 0 1 0 0 1
## 1019 0 1 0 0 1
## .clusterid.fctr startprice.predict.Final.glmnet
## 1396 1 246.09809
## 1418 1 97.93565
## 1764 1 18.36205
## 1234 1 440.94570
## 1019 1 294.49596
## startprice.predict.Final.glmnet.err .label
## 1396 752.9019 11397
## 1418 602.0643 11419
## 1764 481.6380 11765
## 1234 439.9557 11235
## 1019 425.6240 11019
dsp_feats_vctr <- c(NULL)
for(var in grep(".importance", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glb_trnobs_df[glb_trnobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_trnobs_df), value=TRUE)])
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## [1] "startprice.predict.Final.glmnet"
## [2] "startprice.predict.Final.glmnet.err"for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 15 fit.data.training 8 1 423.554 430.935 7.381
## 16 predict.data.new 9 0 430.935 NA NAStep 9.0: predict data new
# Compute final model predictions
# sav_newobs_df <- glb_newobs_df
glb_newobs_df <- glb_get_predictions(glb_newobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
## UniqueID
## 2623 12625
## 2352 12354
## 2100 12102
## 2454 12456
## 2632 12634
## 2094 12096
## description
## 2623 Lot of 10 mixed iPad minis. Colors,models & storage capacity vary between each lot. There may be
## 2352
## 2100
## 2454
## 2632 Good condition IPAD 2 32gb wifi + 3g verizon. LOT OF FIVE.
## 2094
## biddable startprice condition cellular carrier
## 2623 0 999.99 For parts or not working Unknown Unknown
## 2352 1 0.99 Used 0 None
## 2100 1 1.00 New 1 AT&T
## 2454 1 0.99 New 1 T-Mobile
## 2632 0 700.00 Used 1 Verizon
## 2094 0 9.00 Used Unknown Unknown
## color storage productline sold .src .grpid .rnorm idseq.my
## 2623 White Unknown Unknown NA Test <NA> -0.9259777 2625
## 2352 Gold 128 iPad Air 2 NA Test <NA> 0.1545570 2354
## 2100 Space Gray 128 iPad mini 2 NA Test <NA> 2.2673657 2102
## 2454 White 64 iPad Air 2 NA Test <NA> 0.4458716 2456
## 2632 Unknown 32 iPad 2 NA Test <NA> 0.8127608 2634
## 2094 Space Gray 64 iPad Air 2 NA Test <NA> -0.3700640 2096
## prdline.my startprice.log
## 2623 iPadmini 6.90774528
## 2352 iPadAir -0.01005034
## 2100 iPadmini 2+ 0.00000000
## 2454 iPadAir -0.01005034
## 2632 iPad 2 6.55108034
## 2094 iPadAir 2.19722458
## descr.my
## 2623 Lot of 10 mixed iPad minis. Colors, models & storage capacity vary between each lot. There may be
## 2352
## 2100
## 2454
## 2632 Good condition IPAD 2 32gb wifi + 3g verizon. LOT OF FIVE.
## 2094
## condition.fctr cellular.fctr carrier.fctr color.fctr
## 2623 For parts or not working Unknown Unknown White
## 2352 Used 0 None Gold
## 2100 New 1 AT&T Space Gray
## 2454 New 1 T-Mobile White
## 2632 Used 1 Verizon Unknown
## 2094 Used Unknown Unknown Space Gray
## storage.fctr prdline.my.fctr D.terms.n.post.stop
## 2623 Unknown iPadmini 8
## 2352 128 iPadAir 0
## 2100 128 iPadmini 2+ 0
## 2454 64 iPadAir 0
## 2632 32 iPad 2 8
## 2094 64 iPadAir 0
## D.terms.n.post.stop.log D.TfIdf.sum.post.stop D.terms.n.post.stem
## 2623 2.197225 9.127623 8
## 2352 0.000000 0.000000 0
## 2100 0.000000 0.000000 0
## 2454 0.000000 0.000000 0
## 2632 2.197225 7.047501 8
## 2094 0.000000 0.000000 0
## D.terms.n.post.stem.log D.TfIdf.sum.post.stem
## 2623 2.197225 8.069403
## 2352 0.000000 0.000000
## 2100 0.000000 0.000000
## 2454 0.000000 0.000000
## 2632 2.197225 6.969581
## 2094 0.000000 0.000000
## D.terms.n.stem.stop.Ratio D.TfIdf.sum.stem.stop.Ratio D.T.condit
## 2623 1 0.8840640 0.0000000
## 2352 1 1.0000000 0.0000000
## 2100 1 1.0000000 0.0000000
## 2454 1 1.0000000 0.0000000
## 2632 1 0.9889437 0.3026733
## 2094 1 1.0000000 0.0000000
## D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen D.T.great
## 2623 0 0 0 0.0000000 0.3908446 0 0
## 2352 0 0 0 0.0000000 0.0000000 0 0
## 2100 0 0 0 0.0000000 0.0000000 0 0
## 2454 0 0 0 0.0000000 0.0000000 0 0
## 2632 0 0 0 0.4691913 0.4397002 0 0
## 2094 0 0 0 0.0000000 0.0000000 0 0
## D.T.work D.T.excel D.nwrds.log D.nwrds.unq.log D.sum.TfIdf
## 2623 0 0 2.944439 2.197225 8.069403
## 2352 0 0 0.000000 0.000000 0.000000
## 2100 0 0 0.000000 0.000000 0.000000
## 2454 0 0 0.000000 0.000000 0.000000
## 2632 0 0 2.484907 2.197225 6.969581
## 2094 0 0 0.000000 0.000000 0.000000
## D.ratio.sum.TfIdf.nwrds D.nchrs.log D.nuppr.log D.ndgts.log
## 2623 0.4483002 4.634729 4.356709 1.098612
## 2352 0.0000000 0.000000 0.000000 0.000000
## 2100 0.0000000 0.000000 0.000000 0.000000
## 2454 0.0000000 0.000000 0.000000 0.000000
## 2632 0.6335983 4.077537 3.713572 1.609438
## 2094 0.0000000 0.000000 0.000000 0.000000
## D.npnct01.log D.npnct03.log D.npnct05.log D.npnct06.log D.npnct08.log
## 2623 0 0 0 0.6931472 0
## 2352 0 0 0 0.0000000 0
## 2100 0 0 0 0.0000000 0
## 2454 0 0 0 0.0000000 0
## 2632 0 0 0 0.0000000 0
## 2094 0 0 0 0.0000000 0
## D.npnct09.log D.npnct10.log D.npnct11.log D.npnct12.log D.npnct13.log
## 2623 0 0.0000000 0.6931472 0 1.098612
## 2352 0 0.0000000 0.0000000 0 0.000000
## 2100 0 0.0000000 0.0000000 0 0.000000
## 2454 0 0.0000000 0.0000000 0 0.000000
## 2632 0 0.6931472 0.0000000 0 1.098612
## 2094 0 0.0000000 0.0000000 0 0.000000
## D.npnct14.log D.npnct15.log D.npnct16.log D.npnct24.log D.npnct28.log
## 2623 0 0 0.6931472 0.6931472 0
## 2352 0 0 0.0000000 0.0000000 0
## 2100 0 0 0.0000000 0.0000000 0
## 2454 0 0 0.0000000 0.0000000 0
## 2632 0 0 0.0000000 0.6931472 0
## 2094 0 0 0.0000000 0.0000000 0
## D.nstopwrds.log D.ratio.nstopwrds.nwrds D.P.mini D.P.air .clusterid
## 2623 2.1972246 0.4736842 1 0 4
## 2352 0.0000000 1.0000000 0 0 1
## 2100 0.0000000 1.0000000 0 0 1
## 2454 0.0000000 1.0000000 0 0 1
## 2632 0.6931472 0.1666667 0 0 2
## 2094 0.0000000 1.0000000 0 0 1
## .clusterid.fctr startprice.predict.Final.glmnet
## 2623 4 147.8207
## 2352 1 464.9190
## 2100 1 456.8585
## 2454 1 432.6484
## 2632 2 278.9357
## 2094 1 415.4564
## startprice.predict.Final.glmnet.err
## 2623 852.1693
## 2352 463.9290
## 2100 455.8585
## 2454 431.6584
## 2632 421.0643
## 2094 406.4564if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_newobs_df, mdl_id =
## glb_fin_mdl_id): Limiting important feature scatter plots to 5 out of 42
## UniqueID
## 2623 12625
## 2352 12354
## 2100 12102
## 2454 12456
## 2632 12634
## description
## 2623 Lot of 10 mixed iPad minis. Colors,models & storage capacity vary between each lot. There may be
## 2352
## 2100
## 2454
## 2632 Good condition IPAD 2 32gb wifi + 3g verizon. LOT OF FIVE.
## biddable startprice condition cellular carrier
## 2623 0 999.99 For parts or not working Unknown Unknown
## 2352 1 0.99 Used 0 None
## 2100 1 1.00 New 1 AT&T
## 2454 1 0.99 New 1 T-Mobile
## 2632 0 700.00 Used 1 Verizon
## color storage productline sold .src .grpid .rnorm idseq.my
## 2623 White Unknown Unknown NA Test <NA> -0.9259777 2625
## 2352 Gold 128 iPad Air 2 NA Test <NA> 0.1545570 2354
## 2100 Space Gray 128 iPad mini 2 NA Test <NA> 2.2673657 2102
## 2454 White 64 iPad Air 2 NA Test <NA> 0.4458716 2456
## 2632 Unknown 32 iPad 2 NA Test <NA> 0.8127608 2634
## prdline.my startprice.log
## 2623 iPadmini 6.90774528
## 2352 iPadAir -0.01005034
## 2100 iPadmini 2+ 0.00000000
## 2454 iPadAir -0.01005034
## 2632 iPad 2 6.55108034
## descr.my
## 2623 Lot of 10 mixed iPad minis. Colors, models & storage capacity vary between each lot. There may be
## 2352
## 2100
## 2454
## 2632 Good condition IPAD 2 32gb wifi + 3g verizon. LOT OF FIVE.
## condition.fctr cellular.fctr carrier.fctr color.fctr
## 2623 For parts or not working Unknown Unknown White
## 2352 Used 0 None Gold
## 2100 New 1 AT&T Space Gray
## 2454 New 1 T-Mobile White
## 2632 Used 1 Verizon Unknown
## storage.fctr prdline.my.fctr D.terms.n.post.stop
## 2623 Unknown iPadmini 8
## 2352 128 iPadAir 0
## 2100 128 iPadmini 2+ 0
## 2454 64 iPadAir 0
## 2632 32 iPad 2 8
## D.terms.n.post.stop.log D.TfIdf.sum.post.stop D.terms.n.post.stem
## 2623 2.197225 9.127623 8
## 2352 0.000000 0.000000 0
## 2100 0.000000 0.000000 0
## 2454 0.000000 0.000000 0
## 2632 2.197225 7.047501 8
## D.terms.n.post.stem.log D.TfIdf.sum.post.stem
## 2623 2.197225 8.069403
## 2352 0.000000 0.000000
## 2100 0.000000 0.000000
## 2454 0.000000 0.000000
## 2632 2.197225 6.969581
## D.terms.n.stem.stop.Ratio D.TfIdf.sum.stem.stop.Ratio D.T.condit
## 2623 1 0.8840640 0.0000000
## 2352 1 1.0000000 0.0000000
## 2100 1 1.0000000 0.0000000
## 2454 1 1.0000000 0.0000000
## 2632 1 0.9889437 0.3026733
## D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen D.T.great
## 2623 0 0 0 0.0000000 0.3908446 0 0
## 2352 0 0 0 0.0000000 0.0000000 0 0
## 2100 0 0 0 0.0000000 0.0000000 0 0
## 2454 0 0 0 0.0000000 0.0000000 0 0
## 2632 0 0 0 0.4691913 0.4397002 0 0
## D.T.work D.T.excel D.nwrds.log D.nwrds.unq.log D.sum.TfIdf
## 2623 0 0 2.944439 2.197225 8.069403
## 2352 0 0 0.000000 0.000000 0.000000
## 2100 0 0 0.000000 0.000000 0.000000
## 2454 0 0 0.000000 0.000000 0.000000
## 2632 0 0 2.484907 2.197225 6.969581
## D.ratio.sum.TfIdf.nwrds D.nchrs.log D.nuppr.log D.ndgts.log
## 2623 0.4483002 4.634729 4.356709 1.098612
## 2352 0.0000000 0.000000 0.000000 0.000000
## 2100 0.0000000 0.000000 0.000000 0.000000
## 2454 0.0000000 0.000000 0.000000 0.000000
## 2632 0.6335983 4.077537 3.713572 1.609438
## D.npnct01.log D.npnct03.log D.npnct05.log D.npnct06.log D.npnct08.log
## 2623 0 0 0 0.6931472 0
## 2352 0 0 0 0.0000000 0
## 2100 0 0 0 0.0000000 0
## 2454 0 0 0 0.0000000 0
## 2632 0 0 0 0.0000000 0
## D.npnct09.log D.npnct10.log D.npnct11.log D.npnct12.log D.npnct13.log
## 2623 0 0.0000000 0.6931472 0 1.098612
## 2352 0 0.0000000 0.0000000 0 0.000000
## 2100 0 0.0000000 0.0000000 0 0.000000
## 2454 0 0.0000000 0.0000000 0 0.000000
## 2632 0 0.6931472 0.0000000 0 1.098612
## D.npnct14.log D.npnct15.log D.npnct16.log D.npnct24.log D.npnct28.log
## 2623 0 0 0.6931472 0.6931472 0
## 2352 0 0 0.0000000 0.0000000 0
## 2100 0 0 0.0000000 0.0000000 0
## 2454 0 0 0.0000000 0.0000000 0
## 2632 0 0 0.0000000 0.6931472 0
## D.nstopwrds.log D.ratio.nstopwrds.nwrds D.P.mini D.P.air .clusterid
## 2623 2.1972246 0.4736842 1 0 4
## 2352 0.0000000 1.0000000 0 0 1
## 2100 0.0000000 1.0000000 0 0 1
## 2454 0.0000000 1.0000000 0 0 1
## 2632 0.6931472 0.1666667 0 0 2
## .clusterid.fctr startprice.predict.Final.glmnet
## 2623 4 147.8207
## 2352 1 464.9190
## 2100 1 456.8585
## 2454 1 432.6484
## 2632 2 278.9357
## startprice.predict.Final.glmnet.err .label
## 2623 852.1693 12625
## 2352 463.9290 12354
## 2100 455.8585 12102
## 2454 431.6584 12456
## 2632 421.0643 12634
if (glb_is_classification && glb_is_binomial) {
submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id, ".prob"))]
names(submit_df)[2] <- "Probability1"
# submit_df <- glb_newobs_df[, c(paste0(glb_rsp_var_out, glb_fin_mdl_id)), FALSE]
# names(submit_df)[1] <- "BDscience"
# submit_df$BDscience <- as.numeric(submit_df$BDscience) - 1
# #submit_df <-rbind(submit_df, data.frame(bdanalytics=c(" ")))
# print("Submission Stats:")
# print(table(submit_df$BDscience, useNA = "ifany"))
glb_force_prediction_lst <- list()
glb_force_prediction_lst[["0"]] <- c(11885, 11907, 12253, 12585)
for (obs_id in glb_force_prediction_lst[["0"]])
submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
max(0, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] - 0.5)
glb_force_prediction_lst[["1"]] <- c(11871, 11875, 11886,
11913, 11931, 11937, 11967, 11990, 11994, 11999,
12000, 12021, 12065, 12072, 12111, 12114, 12126,
12214, 12233, 12278, 12299, 12446, 12491,
12505, 12576, 12630)
for (obs_id in glb_force_prediction_lst[["1"]])
submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
min(0.9999, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] + 0.5)
} else submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id))]
if (glb_is_classification) {
rsp_var_out <- paste0(glb_rsp_var_out, glb_fin_mdl_id)
tmp_newobs_df <- subset(glb_newobs_df[, c(glb_id_var, ".grpid", rsp_var_out)],
!is.na(.grpid))
tmp_newobs_df <- merge(tmp_newobs_df, dupgrps_df, by=".grpid", all.x=TRUE)
tmp_newobs_df <- merge(tmp_newobs_df, submit_df, by=glb_id_var, all.x = TRUE)
tmp_newobs_df$.err <-
((tmp_newobs_df$Probability1 >= 0.5) & (tmp_newobs_df$sold.0 > 0) |
(tmp_newobs_df$Probability1 <= 0.5) & (tmp_newobs_df$sold.1 > 0))
tmp_newobs_df <- orderBy(~UniqueID, subset(tmp_newobs_df, .err == TRUE))
print("Prediction errors in duplicates:")
print(tmp_newobs_df)
if (nrow(tmp_newobs_df) > 0)
stop("check Prediction errors in duplicates")
#print(dupobs_df[dupobs_df$.grpid == 26, ])
if (max(glb_newobs_df[!is.na(glb_newobs_df[, rsp_var_out]) &
(glb_newobs_df[, rsp_var_out] == "Y"), "startprice"]) >
max(glb_allobs_df[!is.na(glb_allobs_df[, glb_rsp_var]) &
(glb_allobs_df[, glb_rsp_var] == "Y"), "startprice"]))
stop("startprice for some +ve predictions > 675")
}
submit_fname <- paste0(gsub(".", "_", paste0(glb_out_pfx, glb_fin_mdl_id), fixed=TRUE),
"_submit.csv")
write.csv(submit_df, submit_fname, quote=FALSE, row.names=FALSE)
#cat(" ", "\n", file=submit_fn, append=TRUE)
# print(orderBy(~ -max.auc.OOB, glb_models_df[, c("model_id",
# "max.auc.OOB", "max.Accuracy.OOB")]))
for (txt_var in glb_txt_vars) {
# Print post-stem-words but need post-stop-words for debugging ?
print(sprintf(" All post-stem-words TfIDf terms for %s:", txt_var))
myprint_df(glb_post_stem_words_terms_df_lst[[txt_var]])
TfIdf_mtrx <- glb_post_stem_words_TfIdf_mtrx_lst[[txt_var]]
print(glb_allobs_df[
which(TfIdf_mtrx[, tail(glb_post_stem_words_terms_df_lst[[txt_var]], 1)$pos] > 0),
c(glb_id_var, glb_txt_vars)])
print(nrow(subset(glb_post_stem_words_terms_df_lst[[txt_var]], freq == 1)))
#print(glb_allobs_df[which(TfIdf_mtrx[, 207] > 0), c(glb_id_var, glb_txt_vars)])
#unlist(strsplit(glb_allobs_df[2157, "description"], ""))
#glb_allobs_df[2442, c(glb_id_var, glb_txt_vars)]
#TfIdf_mtrx[2442, TfIdf_mtrx[2442, ] > 0]
print(sprintf(" Top_n post_stem_words TfIDf terms for %s:", txt_var))
tmp_df <- glb_post_stem_words_terms_df_lst[[txt_var]]
top_n_vctr <- tmp_df$term[1:glb_top_n[[txt_var]]]
tmp_freq1_df <- subset(tmp_df, freq == 1)
tmp_freq1_df$top_n <- grepl(paste0(top_n_vctr, collapse="|"), tmp_freq1_df$term)
print(subset(tmp_freq1_df, top_n == TRUE))
}## [1] " All post-stem-words TfIDf terms for descr.my:"
## TfIdf term freq pos
## condit 208.1066 condit 496 122
## use 146.5910 use 291 559
## scratch 128.3886 scratch 286 457
## new 125.5866 new 156 346
## good 121.0564 good 197 233
## ipad 107.4871 ipad 232 275
## TfIdf term freq pos
## well 10.609528 well 13 575
## iphon 3.557343 iphon 2 276
## thorough 2.190401 thorough 2 531
## close 1.895930 close 1 115
## photograph 1.421948 photograph 1 388
## forgot 1.263954 forgot 1 211
## TfIdf term freq pos
## remot 1.2639536 remot 1 437
## ringer 1.2639536 ringer 1 450
## septemb 1.2639536 septemb 1 468
## site 1.2639536 site 1 487
## 975 1.1375583 975 1 16
## 79in 0.9479652 79in 1 15
## UniqueID
## 520 10520
## descr.my
## 520 Apple iPad mini 1st Generation 16GB, Wi- Fi, 7.9in - Space Gray, great condition comes with the
## [1] 123
## [1] " Top_n post_stem_words TfIDf terms for descr.my:"
## [1] TfIdf term freq pos top_n
## <0 rows> (or 0-length row.names)if (glb_is_classification && glb_is_binomial)
print(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"])
print(sprintf("glb_sel_mdl_id: %s", glb_sel_mdl_id))## [1] "glb_sel_mdl_id: All.X.glmnet"print(sprintf("glb_fin_mdl_id: %s", glb_fin_mdl_id))## [1] "glb_fin_mdl_id: Final.glmnet"print(dim(glb_fitobs_df))## [1] 1859 70print(dsp_models_df)## model_id min.RMSE.OOB max.R.sq.OOB
## 11 All.X.glmnet 115.1109 0.5586710646
## 10 All.X.bayesglm 116.0511 0.5514321729
## 8 All.X.lm 116.1134 0.5509503685
## 9 All.X.glm 116.1134 0.5509503685
## 7 Low.cor.X.lm 116.2442 0.5499382378
## 13 All.X.no.rnorm.rf 116.5649 0.5474903751
## 23 csm.glmnet 117.0369 0.5437790872
## 22 csm.bayesglm 117.2540 0.5420843077
## 20 csm.lm 117.2598 0.5420391989
## 21 csm.glm 117.2598 0.5420391989
## 25 csm.rf 117.6358 0.5391101087
## 17 All.Interact.X.glmnet 117.9834 0.5363701270
## 19 All.Interact.X.no.rnorm.rf 118.7819 0.5301107517
## 16 All.Interact.X.bayesglm 119.1099 0.5274745018
## 14 All.Interact.X.lm 119.1518 0.5271413171
## 15 All.Interact.X.glm 119.1518 0.5271413171
## 3 Max.cor.Y.cv.0.cp.0.rpart 128.4084 0.4508173959
## 5 Max.cor.Y.lm 130.8154 0.4300359559
## 6 Interact.High.cor.Y.lm 131.0606 0.4278967451
## 18 All.Interact.X.no.rnorm.rpart 142.7501 0.3212929245
## 12 All.X.no.rnorm.rpart 142.7899 0.3209142767
## 24 csm.rpart 142.7899 0.3209142767
## 4 Max.cor.Y.rpart 142.7899 0.3209142767
## 2 Max.cor.Y.cv.0.rpart 173.2747 0.0000000000
## 1 MFO.lm 173.3545 -0.0009216371
## max.Adj.R.sq.fit
## 11 NA
## 10 NA
## 8 0.6550485788
## 9 NA
## 7 0.6523458225
## 13 NA
## 23 NA
## 22 NA
## 20 0.6019349714
## 21 NA
## 25 NA
## 17 NA
## 19 NA
## 16 NA
## 14 0.6748153092
## 15 NA
## 3 NA
## 5 0.4824182255
## 6 0.4904623716
## 18 NA
## 12 NA
## 24 NA
## 4 NA
## 2 NA
## 1 -0.0004792931if (glb_is_regression) {
print(sprintf("%s OOB RMSE: %0.4f", glb_sel_mdl_id,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "min.RMSE.OOB"]))
if (!is.null(glb_category_var)) {
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_var, glb_rsp_var,
predct_error_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "error.abs.OOB"
sOOB_ctgry_df <- dplyr::group_by(tmp_OOBobs_df, prdline.my)
sOOB_ctgry_df <- dplyr::count(sOOB_ctgry_df,
startprice.OOB.sum = sum(startprice),
err.abs.OOB.sum = sum(error.abs.OOB),
err.abs.OOB.mean = mean(error.abs.OOB))
names(sOOB_ctgry_df)[4] <- ".n.OOB"
sOOB_ctgry_df <- dplyr::ungroup(sOOB_ctgry_df)
#intersect(names(glb_ctgry_df), names(sOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, sOOB_ctgry_df, all=TRUE)
print(orderBy(~-err.abs.OOB.mean, glb_ctgry_df))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
pred_stats_df <-
mypredict_mdl(mdl=glb_models_lst[[glb_fin_mdl_id]],
df=glb_newobs_df,
rsp_var=glb_rsp_var,
rsp_var_out=glb_rsp_var_out,
model_id_method=glb_fin_mdl_id,
label="new",
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize,
ret_type="stats")
print(sprintf("%s prediction stats for glb_newobs_df:", glb_fin_mdl_id))
print(pred_stats_df)
}
}## [1] "All.X.glmnet OOB RMSE: 115.1109"
## .n.OOB prdline.my .n.Tst .freqRatio.Tst .freqRatio.OOB
## 1 87 Unknown 87 0.1090226 0.1090226
## 6 137 iPadAir 137 0.1716792 0.1716792
## 3 94 iPadmini 2+ 94 0.1177945 0.1177945
## 4 114 iPadmini 114 0.1428571 0.1428571
## 5 123 iPad 3+ 123 0.1541353 0.1541353
## 7 154 iPad 2 154 0.1929825 0.1929825
## 2 89 iPad 1 89 0.1115288 0.1115288
## startprice.OOB.sum err.abs.OOB.sum err.abs.OOB.mean
## 1 16757.67 10453.482 120.15496
## 6 50210.71 14380.967 104.97056
## 3 26052.46 9597.881 102.10512
## 4 17374.26 8439.230 74.02833
## 5 27442.37 9063.681 73.68846
## 7 21937.85 8523.298 55.34609
## 2 6718.36 4715.722 52.98564
## [1] "Final.glmnet prediction stats for glb_newobs_df:"
## model_id max.R.sq.new min.RMSE.new
## 1 Final.glmnet 0.5586711 115.1109if (glb_is_classification) {
print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id))
print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[, glb_rsp_var])$table))
if (!is.null(glb_category_var)) {
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_var, predct_accurate_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
#intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
print(glb_OOBobs_df[(glb_OOBobs_df$prdline.my == "iPadAir") &
!(glb_OOBobs_df[, predct_accurate_var_name]),
c(glb_id_var, glb_rsp_var_raw,
#"description"
"biddable", "startprice", "condition"
)])
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
print(sprintf("%s new confusion matrix & accuracy: ", glb_fin_mdl_id))
print(t(confusionMatrix(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)],
glb_newobs_df[, glb_rsp_var])$table))
}
}
dsp_myCategory_conf_mtrx <- function(myCategory) {
print(sprintf("%s OOB::myCategory=%s confusion matrix & accuracy: ",
glb_sel_mdl_id, myCategory))
print(t(confusionMatrix(
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, glb_rsp_var])$table))
print(sum(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
predct_accurate_var_name]) /
nrow(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, ]))
err_ids <- glb_OOBobs_df[(glb_OOBobs_df$myCategory == myCategory) &
(!glb_OOBobs_df[, predct_accurate_var_name]), glb_id_var]
OOB_FNerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 1),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FN errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FNerr_df)))
print(OOB_FNerr_df)
OOB_FPerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 0),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FP errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FPerr_df)))
print(OOB_FPerr_df)
}
#dsp_myCategory_conf_mtrx(myCategory="OpEd#Opinion#")
#dsp_myCategory_conf_mtrx(myCategory="Business#Business Day#Dealbook")
#dsp_myCategory_conf_mtrx(myCategory="##")
# if (glb_is_classification) {
# print("FN_OOB_ids:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# print(dsp_vctr <- colSums(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# setdiff(grep("[HSA].", names(glb_OOBobs_df), value=TRUE),
# union(myfind_chr_cols_df(glb_OOBobs_df),
# grep(".fctr", names(glb_OOBobs_df), fixed=TRUE, value=TRUE)))]))
# }
dsp_hdlpfx_results <- function(hdlpfx) {
print(hdlpfx)
print(glb_OOBobs_df[glb_OOBobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_newobs_df), value=TRUE)])
print(dsp_vctr <- colSums(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
setdiff(grep("[HSA]\\.", names(glb_newobs_df), value=TRUE),
union(myfind_chr_cols_df(glb_newobs_df),
grep(".fctr", names(glb_newobs_df), fixed=TRUE, value=TRUE)))]))
print(dsp_vctr <- dsp_vctr[dsp_vctr != 0])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
union(names(dsp_vctr), myfind_chr_cols_df(glb_newobs_df))])
}
#dsp_hdlpfx_results(hdlpfx="Ask Well::")
# print("myMisc::|OpEd|blank|blank|1:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% c(6446),
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# c("WordCount", "WordCount.log", "myMultimedia",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"), ],
# c(glb_rsp_var, "myMultimedia")))
# dsp_chisq.test(Headline.contains="[Vi]deo")
# print(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline")])
# print(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola", Popular=1),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.ConditionalX.y & is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
print(orderBy(as.formula(paste0("~ -", glb_sel_mdl_id, ".importance")), glb_featsimp_df))## All.X.glmnet.importance
## prdline.my.fctriPadAir 100.000000
## condition.fctrNew 89.605213
## D.npnct09.log 83.307569
## prdline.my.fctriPadmini 2+ 78.075306
## condition.fctrNew other (see details) 77.486529
## color.fctrGold 75.718418
## D.npnct10.log 69.483934
## carrier.fctrUnknown 68.783075
## D.terms.n.stem.stop.Ratio 68.180766
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 67.502712
## prdline.my.fctrUnknown:.clusterid.fctr2 67.410570
## D.TfIdf.sum.stem.stop.Ratio 67.012617
## prdline.my.fctriPadmini:.clusterid.fctr5 67.003081
## prdline.my.fctriPad 3+ 66.678300
## D.npnct03.log 65.767488
## condition.fctrManufacturer refurbished 65.196081
## cellular.fctr1 63.071826
## prdline.my.fctriPad 3+:.clusterid.fctr2 63.037048
## D.npnct01.log 62.769601
## prdline.my.fctriPad 1:.clusterid.fctr4 62.057198
## color.fctrWhite 60.273720
## color.fctrSpace Gray 60.150799
## prdline.my.fctriPad 3+:.clusterid.fctr4 58.938150
## prdline.my.fctriPadmini:.clusterid.fctr2 58.335982
## prdline.my.fctriPad 3+:.clusterid.fctr3 57.859425
## idseq.my 57.847819
## .rnorm 57.838522
## D.TfIdf.sum.post.stem 57.838522
## D.TfIdf.sum.post.stop 57.838522
## D.nchrs.log 57.838522
## D.ndgts.log 57.838522
## D.npnct06.log 57.838522
## D.npnct12.log 57.838522
## D.npnct16.log 57.838522
## D.npnct24.log 57.838522
## D.nstopwrds.log 57.838522
## D.nuppr.log 57.838522
## D.nwrds.log 57.838522
## D.nwrds.unq.log 57.838522
## D.ratio.nstopwrds.nwrds 57.838522
## D.sum.TfIdf 57.838522
## D.terms.n.post.stem 57.838522
## D.terms.n.post.stem.log 57.838522
## D.terms.n.post.stop 57.838522
## D.terms.n.post.stop.log 57.838522
## carrier.fctrAT&T 57.838522
## carrier.fctrOther 57.838522
## carrier.fctrT-Mobile 57.838522
## prdline.my.fctrUnknown:.clusterid.fctr4 57.838522
## prdline.my.fctrUnknown:.clusterid.fctr5 57.838522
## prdline.my.fctriPad 1:.clusterid.fctr3 57.838522
## prdline.my.fctriPad 1:.clusterid.fctr5 57.838522
## prdline.my.fctriPad 2:.clusterid.fctr3 57.838522
## prdline.my.fctriPad 2:.clusterid.fctr5 57.838522
## prdline.my.fctriPad 3+:.clusterid.fctr5 57.838522
## prdline.my.fctriPadAir:.clusterid.fctr4 57.838522
## prdline.my.fctriPadAir:.clusterid.fctr5 57.838522
## prdline.my.fctriPadmini 57.838522
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 57.838522
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 57.838522
## prdline.my.fctriPadmini:.clusterid.fctr3 57.838522
## prdline.my.fctriPadmini:.clusterid.fctr4 57.838522
## carrier.fctrVerizon 57.820657
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 57.472389
## prdline.my.fctriPadAir:.clusterid.fctr3 57.312195
## prdline.my.fctriPad 2:.clusterid.fctr2 56.864345
## D.npnct13.log 56.772512
## prdline.my.fctrUnknown:.clusterid.fctr3 55.718052
## D.npnct14.log 55.458341
## D.npnct08.log 55.292038
## color.fctrBlack 55.207252
## D.npnct11.log 55.159939
## D.npnct15.log 54.694738
## condition.fctrSeller refurbished 54.482458
## prdline.my.fctriPad 1:.clusterid.fctr2 54.212030
## D.npnct05.log 53.685990
## D.ratio.sum.TfIdf.nwrds 53.529001
## carrier.fctrSprint 51.737861
## prdline.my.fctriPad 2:.clusterid.fctr4 49.726710
## prdline.my.fctriPad 2 49.578069
## prdline.my.fctriPadAir:.clusterid.fctr2 46.524843
## D.npnct28.log 46.354146
## cellular.fctrUnknown 40.187677
## prdline.my.fctriPad 1 30.878770
## condition.fctrFor parts or not working 26.631947
## storage.fctr64 23.983863
## biddable 16.709485
## storage.fctr32 8.679021
## storage.fctrUnknown 4.194256
## storage.fctr16 0.000000
## importance
## prdline.my.fctriPadAir 100.000000
## condition.fctrNew 89.605213
## D.npnct09.log 83.307569
## prdline.my.fctriPadmini 2+ 78.075306
## condition.fctrNew other (see details) 77.486529
## color.fctrGold 75.718418
## D.npnct10.log 69.483934
## carrier.fctrUnknown 68.783075
## D.terms.n.stem.stop.Ratio 68.180766
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 67.502712
## prdline.my.fctrUnknown:.clusterid.fctr2 67.410570
## D.TfIdf.sum.stem.stop.Ratio 67.012617
## prdline.my.fctriPadmini:.clusterid.fctr5 67.003081
## prdline.my.fctriPad 3+ 66.678300
## D.npnct03.log 65.767488
## condition.fctrManufacturer refurbished 65.196081
## cellular.fctr1 63.071826
## prdline.my.fctriPad 3+:.clusterid.fctr2 63.037048
## D.npnct01.log 62.769601
## prdline.my.fctriPad 1:.clusterid.fctr4 62.057198
## color.fctrWhite 60.273720
## color.fctrSpace Gray 60.150799
## prdline.my.fctriPad 3+:.clusterid.fctr4 58.938150
## prdline.my.fctriPadmini:.clusterid.fctr2 58.335982
## prdline.my.fctriPad 3+:.clusterid.fctr3 57.859425
## idseq.my 57.847819
## .rnorm 57.838522
## D.TfIdf.sum.post.stem 57.838522
## D.TfIdf.sum.post.stop 57.838522
## D.nchrs.log 57.838522
## D.ndgts.log 57.838522
## D.npnct06.log 57.838522
## D.npnct12.log 57.838522
## D.npnct16.log 57.838522
## D.npnct24.log 57.838522
## D.nstopwrds.log 57.838522
## D.nuppr.log 57.838522
## D.nwrds.log 57.838522
## D.nwrds.unq.log 57.838522
## D.ratio.nstopwrds.nwrds 57.838522
## D.sum.TfIdf 57.838522
## D.terms.n.post.stem 57.838522
## D.terms.n.post.stem.log 57.838522
## D.terms.n.post.stop 57.838522
## D.terms.n.post.stop.log 57.838522
## carrier.fctrAT&T 57.838522
## carrier.fctrOther 57.838522
## carrier.fctrT-Mobile 57.838522
## prdline.my.fctrUnknown:.clusterid.fctr4 57.838522
## prdline.my.fctrUnknown:.clusterid.fctr5 57.838522
## prdline.my.fctriPad 1:.clusterid.fctr3 57.838522
## prdline.my.fctriPad 1:.clusterid.fctr5 57.838522
## prdline.my.fctriPad 2:.clusterid.fctr3 57.838522
## prdline.my.fctriPad 2:.clusterid.fctr5 57.838522
## prdline.my.fctriPad 3+:.clusterid.fctr5 57.838522
## prdline.my.fctriPadAir:.clusterid.fctr4 57.838522
## prdline.my.fctriPadAir:.clusterid.fctr5 57.838522
## prdline.my.fctriPadmini 57.838522
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 57.838522
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 57.838522
## prdline.my.fctriPadmini:.clusterid.fctr3 57.838522
## prdline.my.fctriPadmini:.clusterid.fctr4 57.838522
## carrier.fctrVerizon 57.820657
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 57.472389
## prdline.my.fctriPadAir:.clusterid.fctr3 57.312195
## prdline.my.fctriPad 2:.clusterid.fctr2 56.864345
## D.npnct13.log 56.772512
## prdline.my.fctrUnknown:.clusterid.fctr3 55.718052
## D.npnct14.log 55.458341
## D.npnct08.log 55.292038
## color.fctrBlack 55.207252
## D.npnct11.log 55.159939
## D.npnct15.log 54.694738
## condition.fctrSeller refurbished 54.482458
## prdline.my.fctriPad 1:.clusterid.fctr2 54.212030
## D.npnct05.log 53.685990
## D.ratio.sum.TfIdf.nwrds 53.529001
## carrier.fctrSprint 51.737861
## prdline.my.fctriPad 2:.clusterid.fctr4 49.726710
## prdline.my.fctriPad 2 49.578069
## prdline.my.fctriPadAir:.clusterid.fctr2 46.524843
## D.npnct28.log 46.354146
## cellular.fctrUnknown 40.187677
## prdline.my.fctriPad 1 30.878770
## condition.fctrFor parts or not working 26.631947
## storage.fctr64 23.983863
## biddable 16.709485
## storage.fctr32 8.679021
## storage.fctrUnknown 4.194256
## storage.fctr16 0.000000
## Final.glmnet.importance
## prdline.my.fctriPadAir 100.000000
## condition.fctrNew 89.605213
## D.npnct09.log 83.307569
## prdline.my.fctriPadmini 2+ 78.075306
## condition.fctrNew other (see details) 77.486529
## color.fctrGold 75.718418
## D.npnct10.log 69.483934
## carrier.fctrUnknown 68.783075
## D.terms.n.stem.stop.Ratio 68.180766
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 67.502712
## prdline.my.fctrUnknown:.clusterid.fctr2 67.410570
## D.TfIdf.sum.stem.stop.Ratio 67.012617
## prdline.my.fctriPadmini:.clusterid.fctr5 67.003081
## prdline.my.fctriPad 3+ 66.678300
## D.npnct03.log 65.767488
## condition.fctrManufacturer refurbished 65.196081
## cellular.fctr1 63.071826
## prdline.my.fctriPad 3+:.clusterid.fctr2 63.037048
## D.npnct01.log 62.769601
## prdline.my.fctriPad 1:.clusterid.fctr4 62.057198
## color.fctrWhite 60.273720
## color.fctrSpace Gray 60.150799
## prdline.my.fctriPad 3+:.clusterid.fctr4 58.938150
## prdline.my.fctriPadmini:.clusterid.fctr2 58.335982
## prdline.my.fctriPad 3+:.clusterid.fctr3 57.859425
## idseq.my 57.847819
## .rnorm 57.838522
## D.TfIdf.sum.post.stem 57.838522
## D.TfIdf.sum.post.stop 57.838522
## D.nchrs.log 57.838522
## D.ndgts.log 57.838522
## D.npnct06.log 57.838522
## D.npnct12.log 57.838522
## D.npnct16.log 57.838522
## D.npnct24.log 57.838522
## D.nstopwrds.log 57.838522
## D.nuppr.log 57.838522
## D.nwrds.log 57.838522
## D.nwrds.unq.log 57.838522
## D.ratio.nstopwrds.nwrds 57.838522
## D.sum.TfIdf 57.838522
## D.terms.n.post.stem 57.838522
## D.terms.n.post.stem.log 57.838522
## D.terms.n.post.stop 57.838522
## D.terms.n.post.stop.log 57.838522
## carrier.fctrAT&T 57.838522
## carrier.fctrOther 57.838522
## carrier.fctrT-Mobile 57.838522
## prdline.my.fctrUnknown:.clusterid.fctr4 57.838522
## prdline.my.fctrUnknown:.clusterid.fctr5 57.838522
## prdline.my.fctriPad 1:.clusterid.fctr3 57.838522
## prdline.my.fctriPad 1:.clusterid.fctr5 57.838522
## prdline.my.fctriPad 2:.clusterid.fctr3 57.838522
## prdline.my.fctriPad 2:.clusterid.fctr5 57.838522
## prdline.my.fctriPad 3+:.clusterid.fctr5 57.838522
## prdline.my.fctriPadAir:.clusterid.fctr4 57.838522
## prdline.my.fctriPadAir:.clusterid.fctr5 57.838522
## prdline.my.fctriPadmini 57.838522
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 57.838522
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 57.838522
## prdline.my.fctriPadmini:.clusterid.fctr3 57.838522
## prdline.my.fctriPadmini:.clusterid.fctr4 57.838522
## carrier.fctrVerizon 57.820657
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 57.472389
## prdline.my.fctriPadAir:.clusterid.fctr3 57.312195
## prdline.my.fctriPad 2:.clusterid.fctr2 56.864345
## D.npnct13.log 56.772512
## prdline.my.fctrUnknown:.clusterid.fctr3 55.718052
## D.npnct14.log 55.458341
## D.npnct08.log 55.292038
## color.fctrBlack 55.207252
## D.npnct11.log 55.159939
## D.npnct15.log 54.694738
## condition.fctrSeller refurbished 54.482458
## prdline.my.fctriPad 1:.clusterid.fctr2 54.212030
## D.npnct05.log 53.685990
## D.ratio.sum.TfIdf.nwrds 53.529001
## carrier.fctrSprint 51.737861
## prdline.my.fctriPad 2:.clusterid.fctr4 49.726710
## prdline.my.fctriPad 2 49.578069
## prdline.my.fctriPadAir:.clusterid.fctr2 46.524843
## D.npnct28.log 46.354146
## cellular.fctrUnknown 40.187677
## prdline.my.fctriPad 1 30.878770
## condition.fctrFor parts or not working 26.631947
## storage.fctr64 23.983863
## biddable 16.709485
## storage.fctr32 8.679021
## storage.fctrUnknown 4.194256
## storage.fctr16 0.000000print("glb_newobs_df prediction stats:")## [1] "glb_newobs_df prediction stats:"print(myplot_histogram(glb_newobs_df, paste0(glb_rsp_var_out, glb_fin_mdl_id)))## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
if (glb_is_classification)
print(table(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)]))
# players_df <- data.frame(id=c("Chavez", "Giambi", "Menechino", "Myers", "Pena"),
# OBP=c(0.338, 0.391, 0.369, 0.313, 0.361),
# SLG=c(0.540, 0.450, 0.374, 0.447, 0.500),
# cost=c(1400000, 1065000, 295000, 800000, 300000))
# players_df$RS.predict <- predict(glb_models_lst[[csm_mdl_id]], players_df)
# print(orderBy(~ -RS.predict, players_df))
if (length(diff <- setdiff(names(glb_trnobs_df), names(glb_allobs_df))) > 0)
print(diff)
for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
if (length(diff <- setdiff(names(glb_fitobs_df), names(glb_allobs_df))) > 0)
print(diff)
if (length(diff <- setdiff(names(glb_OOBobs_df), names(glb_allobs_df))) > 0)
print(diff)
for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
if (length(diff <- setdiff(names(glb_newobs_df), names(glb_allobs_df))) > 0)
print(diff)
if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "prdnew_dsk.RData"))
rm(submit_df, tmp_OOBobs_df)
# tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn,
# replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
# "data.new.prediction")), flip_coord=TRUE)
# print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())
glb_chunks_df <- myadd_chunk(glb_chunks_df, "display.session.info", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 16 predict.data.new 9 0 430.935 440.288 9.353
## 17 display.session.info 10 0 440.288 NA NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 114.154 391.695 277.541
## 5 extract.features 3 0 14.715 87.760 73.046
## 12 fit.models 7 2 391.695 414.739 23.044
## 10 fit.models 7 0 97.894 114.154 16.260
## 16 predict.data.new 9 0 430.935 440.288 9.353
## 15 fit.data.training 8 1 423.554 430.935 7.381
## 13 fit.models 7 3 414.740 420.743 6.003
## 7 manage.missing.data 4 1 88.810 93.670 4.860
## 8 select.features 5 0 93.670 97.445 3.775
## 14 fit.data.training 8 0 420.743 423.553 2.811
## 1 import.data 1 0 8.739 11.506 2.768
## 2 inspect.data 2 0 11.507 13.552 2.045
## 6 cluster.data 4 0 87.761 88.809 1.048
## 3 scrub.data 2 1 13.553 14.205 0.652
## 4 transform.data 2 2 14.206 14.715 0.509
## 9 partition.data.training 6 0 97.445 97.894 0.449
## duration
## 11 277.541
## 5 73.045
## 12 23.044
## 10 16.260
## 16 9.353
## 15 7.381
## 13 6.003
## 7 4.860
## 8 3.775
## 14 2.810
## 1 2.767
## 2 2.045
## 6 1.048
## 3 0.652
## 4 0.509
## 9 0.449
## [1] "Total Elapsed Time: 440.288 secs"
## R version 3.2.1 (2015-06-18)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.4 (Yosemite)
##
## locale:
## [1] C/en_US.UTF-8/C/C/C/en_US.UTF-8
##
## attached base packages:
## [1] tcltk grid parallel stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] randomForest_4.6-10 glmnet_2.0-2 arm_1.8-6
## [4] lme4_1.1-8 Matrix_1.2-2 MASS_7.3-43
## [7] rpart.plot_1.5.2 rpart_4.1-10 tidyr_0.2.0
## [10] entropy_1.2.1 dynamicTreeCut_1.62 proxy_0.4-15
## [13] reshape2_1.4.1 sqldf_0.4-10 RSQLite_1.0.0
## [16] DBI_0.3.1 tm_0.6-2 NLP_0.1-8
## [19] stringr_1.0.0 gsubfn_0.6-6 proto_0.3-10
## [22] mgcv_1.8-7 nlme_3.1-121 dplyr_0.4.2
## [25] plyr_1.8.3 gdata_2.17.0 doMC_1.3.3
## [28] iterators_1.0.7 foreach_1.4.2 doBy_4.5-13
## [31] survival_2.38-3 caret_6.0-52 ggplot2_1.0.1
## [34] lattice_0.20-33
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.0 gtools_3.5.0 assertthat_0.1
## [4] digest_0.6.8 slam_0.1-32 R6_2.1.0
## [7] BradleyTerry2_1.0-6 chron_2.3-47 stats4_3.2.1
## [10] coda_0.17-1 evaluate_0.7 lazyeval_0.1.10
## [13] minqa_1.2.4 SparseM_1.6 car_2.0-25
## [16] nloptr_1.0.4 rmarkdown_0.7 labeling_0.3
## [19] splines_3.2.1 munsell_0.4.2 compiler_3.2.1
## [22] htmltools_0.2.6 nnet_7.3-10 codetools_0.2-14
## [25] brglm_0.5-9 gtable_0.1.2 magrittr_1.5
## [28] formatR_1.2 scales_0.2.5 stringi_0.5-5
## [31] RColorBrewer_1.1-2 tools_3.2.1 abind_1.4-3
## [34] pbkrtest_0.4-2 yaml_2.1.13 colorspace_1.2-6
## [37] knitr_1.10.5 quantreg_5.11